{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 Data Preprocessing\n",
    "\n",
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a function that read the texts from the folders. While clipping the data into 600 datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_txt_files_into_single_dataframe(folder_path):\n",
    "    data = []\n",
    "    labels = []\n",
    "    \n",
    "    # List all files in the folder\n",
    "    files = os.listdir(folder_path)\n",
    "    prev_patient_id = os.path.splitext(files[0])[0][:-2]\n",
    "    patient_data = []\n",
    "    for file in files:\n",
    "        label = os.path.splitext(file)[0][:-2]\n",
    "        # Read the contents of the file\n",
    "        with open(os.path.join(folder_path, file), 'r') as f:\n",
    "            content = f.read()\n",
    "            numbers = [float(num) for num in content.split()]\n",
    "            if len(numbers) > 2100:\n",
    "                numbers = numbers[0:2100]\n",
    "            # Clip the numbers obtained into 1/10 of its size\n",
    "            numbers = numbers[::10]\n",
    "        # if label is equal to the previous patient id\n",
    "        if prev_patient_id == label:\n",
    "            # Add the numbers to the patient data\n",
    "            patient_data += numbers\n",
    "        # If the label is different from the patient id\n",
    "        else:\n",
    "            # Append the patient_data as a new row in data\n",
    "            data.append(patient_data)\n",
    "            # Re-initialize the patient_data as the numbers list\n",
    "            patient_data = numbers\n",
    "            # Append the previous patient id as a new row in the label\n",
    "            labels.append(prev_patient_id)\n",
    "            # Re-assign the prev_patient_id to the new label\n",
    "            prev_patient_id = label\n",
    "        # print(f\"{prev_patient_id} - {len(patient_data)}\")\n",
    "\n",
    "    # Debugging function\n",
    "    # print(f\"Data: {[len(d) for d in data]}\")\n",
    "    # print(f\"Labels: {labels}\")\n",
    "\n",
    "    data = np.array(data)\n",
    "    # Create a DataFrame\n",
    "    df = pd.DataFrame(data=data)\n",
    "    df['label'] = labels\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_txt_files_into_subpart_dataframe(folder_path):\n",
    "    data = []\n",
    "    labels = []\n",
    "    \n",
    "    # List all files in the folder\n",
    "    files = os.listdir(folder_path)\n",
    "    patient_data = []\n",
    "    for file in files:\n",
    "        label = os.path.splitext(file)[0][:-2]\n",
    "        # Read the contents of the file\n",
    "        with open(os.path.join(folder_path, file), 'r') as f:\n",
    "            content = f.read()\n",
    "            numbers = [float(num) for num in content.split()]\n",
    "            if len(numbers) > 2100:\n",
    "                numbers = numbers[0:2100]\n",
    "            # Clip the numbers obtained into 1/10 of its size\n",
    "            numbers = numbers[::10]\n",
    "        # normalized_signal = detrended_signal / detrended_signal.max()\n",
    "        \n",
    "        data.append(numbers)\n",
    "        # Append the label as a new row in the label\n",
    "        labels.append(int(label))\n",
    "\n",
    "    data = np.array(data)\n",
    "    # Create a DataFrame\n",
    "    df = pd.DataFrame(data=data)\n",
    "    df['subject_id'] = labels\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>201</th>\n",
       "      <th>202</th>\n",
       "      <th>203</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>subject_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>2189.0</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>2138.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>2147.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1942.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>2360.0</td>\n",
       "      <td>2290.0</td>\n",
       "      <td>2315.0</td>\n",
       "      <td>2298.0</td>\n",
       "      <td>2267.0</td>\n",
       "      <td>2248.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2031.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>2077.0</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>2123.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2147.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2064.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>2172.0</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>2199.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2152.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>2167.0</td>\n",
       "      <td>2169.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2085.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>2141.0</td>\n",
       "      <td>2157.0</td>\n",
       "      <td>2186.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>1961.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>2048.0</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>2037.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>2026.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>1939.0</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>657 rows × 211 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0       1       2       3       4       5       6       7       8  \\\n",
       "0    1994.0  1983.0  2027.0  1993.0  1987.0  2000.0  1981.0  1991.0  1980.0   \n",
       "1    1942.0  1957.0  1955.0  1955.0  1932.0  1957.0  1909.0  1934.0  1909.0   \n",
       "2    2003.0  2007.0  1995.0  1970.0  1989.0  1992.0  1973.0  1998.0  1983.0   \n",
       "3    2147.0  2143.0  2164.0  2164.0  2158.0  2139.0  2156.0  2127.0  2134.0   \n",
       "4    1857.0  1875.0  1857.0  1867.0  1863.0  1872.0  1865.0  1937.0  1969.0   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "652  2050.0  2064.0  2004.0  1998.0  2030.0  1995.0  2009.0  1957.0  1995.0   \n",
       "653  2167.0  2169.0  2154.0  2153.0  2086.0  2073.0  2095.0  2039.0  2071.0   \n",
       "654  1961.0  1994.0  1983.0  1987.0  1960.0  1998.0  1966.0  1982.0  1954.0   \n",
       "655  2011.0  2037.0  1999.0  1983.0  2020.0  2007.0  2001.0  2007.0  1984.0   \n",
       "656  1939.0  1953.0  1937.0  1978.0  1962.0  1967.0  1969.0  1960.0  1947.0   \n",
       "\n",
       "          9  ...     201     202     203     204     205     206     207  \\\n",
       "0    1991.0  ...  2231.0  2189.0  2201.0  2185.0  2171.0  2138.0  2110.0   \n",
       "1    1916.0  ...  2367.0  2360.0  2290.0  2315.0  2298.0  2267.0  2248.0   \n",
       "2    1977.0  ...  1913.0  1981.0  1984.0  2047.0  2031.0  2080.0  2077.0   \n",
       "3    2086.0  ...  2120.0  2129.0  2096.0  2118.0  2060.0  2073.0  2078.0   \n",
       "4    1990.0  ...  2039.0  2097.0  2097.0  2139.0  2153.0  2144.0  2154.0   \n",
       "..      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "652  1936.0  ...  2179.0  2172.0  2174.0  2199.0  2196.0  2143.0  2152.0   \n",
       "653  2054.0  ...  1989.0  2047.0  2085.0  2122.0  2141.0  2157.0  2186.0   \n",
       "654  1956.0  ...  2017.0  2024.0  2024.0  2053.0  2044.0  2048.0  2029.0   \n",
       "655  1981.0  ...  1996.0  2026.0  1986.0  1992.0  2023.0  2015.0  1984.0   \n",
       "656  1995.0  ...  1982.0  1988.0  1988.0  1960.0  1938.0  1959.0  1984.0   \n",
       "\n",
       "        208     209  subject_id  \n",
       "0    2147.0  2114.0         100  \n",
       "1    2231.0  2212.0         100  \n",
       "2    2099.0  2123.0         100  \n",
       "3    2047.0  2066.0         103  \n",
       "4    2153.0  2158.0         103  \n",
       "..      ...     ...         ...  \n",
       "652  2160.0  2140.0          99  \n",
       "653  2197.0  2242.0          99  \n",
       "654  2060.0  2059.0           9  \n",
       "655  2000.0  2005.0           9  \n",
       "656  1984.0  1958.0           9  \n",
       "\n",
       "[657 rows x 211 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_path = \"Database\\Data\\PPG\"\n",
    "df1 = read_txt_files_into_subpart_dataframe(folder_path)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example Data from the File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d4fd09a7f0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.fft import fft, fftfreq\n",
    "files = [\"100_3.txt\", \"100_1.txt\", \"100_2.txt\"]\n",
    "ppg = []\n",
    "for file in files:\n",
    "    with open(os.path.join(folder_path, file), 'r') as f:\n",
    "        content = f.read()\n",
    "        numbers = [float(num) for num in content.split()]\n",
    "        if len(numbers) > 2100:\n",
    "            numbers = numbers[0:2100]\n",
    "        ppg += numbers[::10]\n",
    "# Sampling Points\n",
    "N = len(ppg)\n",
    "# Sampling Time\n",
    "T = 1 / 1000 # Every 1 millisecond\n",
    "\n",
    "x = np.linspace(0, N*T, N)\n",
    "y = np.array(ppg)\n",
    "plt.title(\"PPG Signal of Patient 100\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DFT of the PPG signal of Patient 100 is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fftsignal = fft(ppg)\n",
    "fftsignal = fftsignal[: N//2]\n",
    "xf = fftfreq(N, T)[:N//2]\n",
    "plt.ylabel('frequency')\n",
    "plt.xlabel('sample')\n",
    "plt.title(\"FFT of the PPG signal\")\n",
    "plt.plot(xf, 2.0/N * np.abs(fftsignal))\n",
    "plt.xlim((-10, 100))\n",
    "plt.plot(xf, 2.0/N * np.abs(fftsignal))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data from an excel sheet and join it together to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>systolic_blood_pressure</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>heartrate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "      <td>89</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>101</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>415</td>\n",
       "      <td>111</td>\n",
       "      <td>70</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>416</td>\n",
       "      <td>93</td>\n",
       "      <td>57</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>417</td>\n",
       "      <td>120</td>\n",
       "      <td>69</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>418</td>\n",
       "      <td>106</td>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>419</td>\n",
       "      <td>108</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     subject_id  systolic_blood_pressure  diastolic_blood_pressure  heartrate\n",
       "0             2                      161                        89         97\n",
       "1             3                      160                        93         76\n",
       "2             6                      101                        71         79\n",
       "3             8                      136                        93         87\n",
       "4             9                      123                        73         73\n",
       "..          ...                      ...                       ...        ...\n",
       "214         415                      111                        70         77\n",
       "215         416                       93                        57         79\n",
       "216         417                      120                        69         72\n",
       "217         418                      106                        69         67\n",
       "218         419                      108                        68         65\n",
       "\n",
       "[219 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_df = pd.read_excel(\"PPG BP.xlsx\")\n",
    "other_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>systolic_blood_pressure</th>\n",
       "      <th>diastolic_blood_pressure</th>\n",
       "      <th>heartrate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>2138.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1942.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>1932.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2315.0</td>\n",
       "      <td>2298.0</td>\n",
       "      <td>2267.0</td>\n",
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       "    <tr>\n",
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       "      <td>2003.0</td>\n",
       "      <td>2007.0</td>\n",
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       "      <td>1998.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2031.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>2077.0</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>2123.0</td>\n",
       "      <td>100</td>\n",
       "      <td>140</td>\n",
       "      <td>82</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2147.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>103</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>1857.0</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>103</td>\n",
       "      <td>120</td>\n",
       "      <td>60</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2064.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2199.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2152.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>99</td>\n",
       "      <td>133</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>2167.0</td>\n",
       "      <td>2169.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>2141.0</td>\n",
       "      <td>2157.0</td>\n",
       "      <td>2186.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>99</td>\n",
       "      <td>133</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>1961.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>2048.0</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>9</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>2037.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>1939.0</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>9</td>\n",
       "      <td>123</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>657 rows × 214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0       1       2       3       4       5       6       7       8  \\\n",
       "0    1994.0  1983.0  2027.0  1993.0  1987.0  2000.0  1981.0  1991.0  1980.0   \n",
       "1    1942.0  1957.0  1955.0  1955.0  1932.0  1957.0  1909.0  1934.0  1909.0   \n",
       "2    2003.0  2007.0  1995.0  1970.0  1989.0  1992.0  1973.0  1998.0  1983.0   \n",
       "3    2147.0  2143.0  2164.0  2164.0  2158.0  2139.0  2156.0  2127.0  2134.0   \n",
       "4    1857.0  1875.0  1857.0  1867.0  1863.0  1872.0  1865.0  1937.0  1969.0   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "652  2050.0  2064.0  2004.0  1998.0  2030.0  1995.0  2009.0  1957.0  1995.0   \n",
       "653  2167.0  2169.0  2154.0  2153.0  2086.0  2073.0  2095.0  2039.0  2071.0   \n",
       "654  1961.0  1994.0  1983.0  1987.0  1960.0  1998.0  1966.0  1982.0  1954.0   \n",
       "655  2011.0  2037.0  1999.0  1983.0  2020.0  2007.0  2001.0  2007.0  1984.0   \n",
       "656  1939.0  1953.0  1937.0  1978.0  1962.0  1967.0  1969.0  1960.0  1947.0   \n",
       "\n",
       "          9  ...     204     205     206     207     208     209  subject_id  \\\n",
       "0    1991.0  ...  2185.0  2171.0  2138.0  2110.0  2147.0  2114.0         100   \n",
       "1    1916.0  ...  2315.0  2298.0  2267.0  2248.0  2231.0  2212.0         100   \n",
       "2    1977.0  ...  2047.0  2031.0  2080.0  2077.0  2099.0  2123.0         100   \n",
       "3    2086.0  ...  2118.0  2060.0  2073.0  2078.0  2047.0  2066.0         103   \n",
       "4    1990.0  ...  2139.0  2153.0  2144.0  2154.0  2153.0  2158.0         103   \n",
       "..      ...  ...     ...     ...     ...     ...     ...     ...         ...   \n",
       "652  1936.0  ...  2199.0  2196.0  2143.0  2152.0  2160.0  2140.0          99   \n",
       "653  2054.0  ...  2122.0  2141.0  2157.0  2186.0  2197.0  2242.0          99   \n",
       "654  1956.0  ...  2053.0  2044.0  2048.0  2029.0  2060.0  2059.0           9   \n",
       "655  1981.0  ...  1992.0  2023.0  2015.0  1984.0  2000.0  2005.0           9   \n",
       "656  1995.0  ...  1960.0  1938.0  1959.0  1984.0  1984.0  1958.0           9   \n",
       "\n",
       "     systolic_blood_pressure  diastolic_blood_pressure  heartrate  \n",
       "0                        140                        82         73  \n",
       "1                        140                        82         73  \n",
       "2                        140                        82         73  \n",
       "3                        120                        60         76  \n",
       "4                        120                        60         76  \n",
       "..                       ...                       ...        ...  \n",
       "652                      133                        71         80  \n",
       "653                      133                        71         80  \n",
       "654                      123                        73         73  \n",
       "655                      123                        73         73  \n",
       "656                      123                        73         73  \n",
       "\n",
       "[657 rows x 214 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df1, other_df, on='subject_id', how='inner')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing with the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2d4fd08b7f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import signal\n",
    "\n",
    "x = np.linspace(0, N*T, N)\n",
    "y = np.array(ppg)\n",
    "y_normalized = y / np.max(np.abs(y), axis=0)\n",
    "y_detrended =  signal.detrend(y_normalized, axis=0)\n",
    "plt.plot(x, y_normalized, label=\"Normalized\")\n",
    "plt.plot(x, y_detrended, label=\"Detrended\")\n",
    "plt.title(\"PPG Signal of Patient 100\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.plot(x, y_normalized, label=\"Normalized Detrended\")\n",
    "pca = PCA(n_components=0.10)\n",
    "pca_signal = pca.fit_transform(y_normalized.reshape(-1, 1))\n",
    "plt.plot(x, pca_signal, label=\"PCA Signal\")\n",
    "\n",
    "\n",
    "# Define the bandpass filter\n",
    "lowcut = 0.4\n",
    "highcut = 4.0\n",
    "nyquist = 0.5 * len(pca_signal)\n",
    "low = lowcut / nyquist\n",
    "high = highcut / nyquist\n",
    "b, a = signal.butter(4, [low, high], btype='band')\n",
    "\n",
    "# Apply the bandpass filter to the PPG signal\n",
    "filtered_ppg_signal = signal.filtfilt(b, a, pca_signal, axis=0)\n",
    "\n",
    "plt.plot(x, y_normalized, label=\"Filtered Signal\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separate the dataframe into input and target groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1994.0</td>\n",
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       "      <td>2224.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>2189.0</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>2185.0</td>\n",
       "      <td>2171.0</td>\n",
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       "      <td>1932.0</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1916.0</td>\n",
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       "      <td>2383.0</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>2360.0</td>\n",
       "      <td>2290.0</td>\n",
       "      <td>2315.0</td>\n",
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       "      <td>2231.0</td>\n",
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       "      <td>1973.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>1913.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2031.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>2077.0</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>2123.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2147.0</td>\n",
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       "      <td>2164.0</td>\n",
       "      <td>2158.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2165.0</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>2060.0</td>\n",
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       "      <td>2078.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2066.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1857.0</td>\n",
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       "      <td>1857.0</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>2154.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>652</th>\n",
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       "      <td>2064.0</td>\n",
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       "      <td>1998.0</td>\n",
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       "      <td>2009.0</td>\n",
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       "      <td>1936.0</td>\n",
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       "      <td>2183.0</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>2172.0</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>2199.0</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2152.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>2140.0</td>\n",
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       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>2167.0</td>\n",
       "      <td>2169.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2086.0</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>2095.0</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>2054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1914.0</td>\n",
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       "      <td>2047.0</td>\n",
       "      <td>2085.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>2141.0</td>\n",
       "      <td>2157.0</td>\n",
       "      <td>2186.0</td>\n",
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       "      <th>654</th>\n",
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       "      <td>1956.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2067.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2044.0</td>\n",
       "      <td>2048.0</td>\n",
       "      <td>2029.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>2059.0</td>\n",
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       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>2037.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>2026.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2005.0</td>\n",
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       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>1939.0</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1958.0</td>\n",
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       "</table>\n",
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       "</div>"
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       "        0       1       2       3       4       5       6       7       8    \\\n",
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       "1    1942.0  1957.0  1955.0  1955.0  1932.0  1957.0  1909.0  1934.0  1909.0   \n",
       "2    2003.0  2007.0  1995.0  1970.0  1989.0  1992.0  1973.0  1998.0  1983.0   \n",
       "3    2147.0  2143.0  2164.0  2164.0  2158.0  2139.0  2156.0  2127.0  2134.0   \n",
       "4    1857.0  1875.0  1857.0  1867.0  1863.0  1872.0  1865.0  1937.0  1969.0   \n",
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       "\n",
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       "4    1990.0  ...  2000.0  2039.0  2097.0  2097.0  2139.0  2153.0  2144.0   \n",
       "..      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "652  1936.0  ...  2183.0  2179.0  2172.0  2174.0  2199.0  2196.0  2143.0   \n",
       "653  2054.0  ...  1914.0  1989.0  2047.0  2085.0  2122.0  2141.0  2157.0   \n",
       "654  1956.0  ...  2067.0  2017.0  2024.0  2024.0  2053.0  2044.0  2048.0   \n",
       "655  1981.0  ...  2035.0  1996.0  2026.0  1986.0  1992.0  2023.0  2015.0   \n",
       "656  1995.0  ...  1957.0  1982.0  1988.0  1988.0  1960.0  1938.0  1959.0   \n",
       "\n",
       "        207     208     209  \n",
       "0    2110.0  2147.0  2114.0  \n",
       "1    2248.0  2231.0  2212.0  \n",
       "2    2077.0  2099.0  2123.0  \n",
       "3    2078.0  2047.0  2066.0  \n",
       "4    2154.0  2153.0  2158.0  \n",
       "..      ...     ...     ...  \n",
       "652  2152.0  2160.0  2140.0  \n",
       "653  2186.0  2197.0  2242.0  \n",
       "654  2029.0  2060.0  2059.0  \n",
       "655  1984.0  2000.0  2005.0  \n",
       "656  1984.0  1984.0  1958.0  \n",
       "\n",
       "[657 rows x 210 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_dropped_columns = [\"systolic_blood_pressure\", \"diastolic_blood_pressure\", \"heartrate\", \"subject_id\"]\n",
    "\n",
    "target_df = df[[\"systolic_blood_pressure\", \"diastolic_blood_pressure\", \"heartrate\"]]\n",
    "input_df = df.drop(list_of_dropped_columns, axis=1)\n",
    "input_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a MinMaxScaler to scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.249968</td>\n",
       "      <td>1.136469</td>\n",
       "      <td>0.970300</td>\n",
       "      <td>0.862789</td>\n",
       "      <td>0.760773</td>\n",
       "      <td>0.660322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.419341</td>\n",
       "      <td>-0.342687</td>\n",
       "      <td>-0.367720</td>\n",
       "      <td>-0.459159</td>\n",
       "      <td>-0.351794</td>\n",
       "      <td>-0.313729</td>\n",
       "      <td>-0.399722</td>\n",
       "      <td>-0.254880</td>\n",
       "      <td>-0.333882</td>\n",
       "      <td>-0.362776</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102053</td>\n",
       "      <td>-0.050783</td>\n",
       "      <td>0.256417</td>\n",
       "      <td>0.261463</td>\n",
       "      <td>0.527942</td>\n",
       "      <td>0.437047</td>\n",
       "      <td>0.640048</td>\n",
       "      <td>0.612196</td>\n",
       "      <td>0.687604</td>\n",
       "      <td>0.771689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.621730</td>\n",
       "      <td>0.678990</td>\n",
       "      <td>0.839417</td>\n",
       "      <td>0.890584</td>\n",
       "      <td>0.925746</td>\n",
       "      <td>0.877343</td>\n",
       "      <td>1.013014</td>\n",
       "      <td>0.898887</td>\n",
       "      <td>0.962225</td>\n",
       "      <td>0.725424</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732204</td>\n",
       "      <td>0.495387</td>\n",
       "      <td>0.509334</td>\n",
       "      <td>0.337366</td>\n",
       "      <td>0.410842</td>\n",
       "      <td>0.129684</td>\n",
       "      <td>0.174816</td>\n",
       "      <td>0.183939</td>\n",
       "      <td>0.046147</td>\n",
       "      <td>0.115187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.634641</td>\n",
       "      <td>-0.504225</td>\n",
       "      <td>-0.566132</td>\n",
       "      <td>-0.503422</td>\n",
       "      <td>-0.512421</td>\n",
       "      <td>-0.451746</td>\n",
       "      <td>-0.485136</td>\n",
       "      <td>-0.101079</td>\n",
       "      <td>0.071028</td>\n",
       "      <td>0.193441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132955</td>\n",
       "      <td>0.308650</td>\n",
       "      <td>0.559428</td>\n",
       "      <td>0.541897</td>\n",
       "      <td>0.702218</td>\n",
       "      <td>0.739326</td>\n",
       "      <td>0.678854</td>\n",
       "      <td>0.703749</td>\n",
       "      <td>0.676450</td>\n",
       "      <td>0.678985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>0.291743</td>\n",
       "      <td>0.420638</td>\n",
       "      <td>0.203283</td>\n",
       "      <td>0.209655</td>\n",
       "      <td>0.400864</td>\n",
       "      <td>0.261913</td>\n",
       "      <td>0.359508</td>\n",
       "      <td>0.112391</td>\n",
       "      <td>0.320146</td>\n",
       "      <td>0.009702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.785184</td>\n",
       "      <td>0.745065</td>\n",
       "      <td>0.678557</td>\n",
       "      <td>0.666653</td>\n",
       "      <td>0.744708</td>\n",
       "      <td>0.703382</td>\n",
       "      <td>0.448111</td>\n",
       "      <td>0.470258</td>\n",
       "      <td>0.486729</td>\n",
       "      <td>0.387075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>0.635121</td>\n",
       "      <td>0.719731</td>\n",
       "      <td>0.719646</td>\n",
       "      <td>0.764107</td>\n",
       "      <td>0.500722</td>\n",
       "      <td>0.474074</td>\n",
       "      <td>0.621933</td>\n",
       "      <td>0.360866</td>\n",
       "      <td>0.544909</td>\n",
       "      <td>0.469774</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.362263</td>\n",
       "      <td>-0.007922</td>\n",
       "      <td>0.247557</td>\n",
       "      <td>0.412837</td>\n",
       "      <td>0.553656</td>\n",
       "      <td>0.615626</td>\n",
       "      <td>0.667171</td>\n",
       "      <td>0.774697</td>\n",
       "      <td>0.795535</td>\n",
       "      <td>0.961883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>-0.127245</td>\n",
       "      <td>0.076410</td>\n",
       "      <td>0.067329</td>\n",
       "      <td>0.116538</td>\n",
       "      <td>0.018972</td>\n",
       "      <td>0.231815</td>\n",
       "      <td>0.093219</td>\n",
       "      <td>0.194807</td>\n",
       "      <td>0.052029</td>\n",
       "      <td>0.068374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144798</td>\n",
       "      <td>-0.111080</td>\n",
       "      <td>-0.098317</td>\n",
       "      <td>-0.106552</td>\n",
       "      <td>0.009447</td>\n",
       "      <td>-0.046311</td>\n",
       "      <td>-0.038741</td>\n",
       "      <td>-0.131644</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>-0.013170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>0.104163</td>\n",
       "      <td>0.282230</td>\n",
       "      <td>0.159517</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>0.330975</td>\n",
       "      <td>0.299504</td>\n",
       "      <td>0.296779</td>\n",
       "      <td>0.350880</td>\n",
       "      <td>0.239538</td>\n",
       "      <td>0.233117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132279</td>\n",
       "      <td>-0.069772</td>\n",
       "      <td>0.052309</td>\n",
       "      <td>-0.144815</td>\n",
       "      <td>-0.131977</td>\n",
       "      <td>-0.005740</td>\n",
       "      <td>-0.051106</td>\n",
       "      <td>-0.195408</td>\n",
       "      <td>-0.123638</td>\n",
       "      <td>-0.110192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>-0.305879</td>\n",
       "      <td>-0.187144</td>\n",
       "      <td>-0.227957</td>\n",
       "      <td>-0.013031</td>\n",
       "      <td>-0.061171</td>\n",
       "      <td>-0.011288</td>\n",
       "      <td>0.016545</td>\n",
       "      <td>-0.011968</td>\n",
       "      <td>-0.078449</td>\n",
       "      <td>0.187492</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.204048</td>\n",
       "      <td>-0.093056</td>\n",
       "      <td>-0.083934</td>\n",
       "      <td>-0.091053</td>\n",
       "      <td>-0.233653</td>\n",
       "      <td>-0.342134</td>\n",
       "      <td>-0.253696</td>\n",
       "      <td>-0.151273</td>\n",
       "      <td>-0.147495</td>\n",
       "      <td>-0.261391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>657 rows × 210 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "0   -0.383990 -0.374659 -0.149953 -0.280419 -0.289646 -0.203925 -0.289041   \n",
       "1   -0.018908  0.107613  0.139787  0.171493  0.094609  0.244117  0.023393   \n",
       "2   -0.419341 -0.342687 -0.367720 -0.459159 -0.351794 -0.313729 -0.399722   \n",
       "3    0.621730  0.678990  0.839417  0.890584  0.925746  0.877343  1.013014   \n",
       "4   -0.634641 -0.504225 -0.566132 -0.503422 -0.512421 -0.451746 -0.485136   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "652  0.291743  0.420638  0.203283  0.209655  0.400864  0.261913  0.359508   \n",
       "653  0.635121  0.719731  0.719646  0.764107  0.500722  0.474074  0.621933   \n",
       "654 -0.127245  0.076410  0.067329  0.116538  0.018972  0.231815  0.093219   \n",
       "655  0.104163  0.282230  0.159517  0.118743  0.330975  0.299504  0.296779   \n",
       "656 -0.305879 -0.187144 -0.227957 -0.013031 -0.061171 -0.011288  0.016545   \n",
       "\n",
       "          7         8         9    ...       200       201       202  \\\n",
       "0   -0.223277 -0.283293 -0.221798  ...  0.972762  0.986255  0.752103   \n",
       "1    0.169626  0.041330  0.083798  ...  1.739553  1.631012  1.550244   \n",
       "2   -0.254880 -0.333882 -0.362776  ... -0.102053 -0.050783  0.256417   \n",
       "3    0.898887  0.962225  0.725424  ...  0.732204  0.495387  0.509334   \n",
       "4   -0.101079  0.071028  0.193441  ...  0.132955  0.308650  0.559428   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "652  0.112391  0.320146  0.009702  ...  0.785184  0.745065  0.678557   \n",
       "653  0.360866  0.544909  0.469774  ... -0.362263 -0.007922  0.247557   \n",
       "654  0.194807  0.052029  0.068374  ...  0.144798 -0.111080 -0.098317   \n",
       "655  0.350880  0.239538  0.233117  ...  0.132279 -0.069772  0.052309   \n",
       "656 -0.011968 -0.078449  0.187492  ... -0.204048 -0.093056 -0.083934   \n",
       "\n",
       "          203       204       205       206       207       208       209  \n",
       "0    0.788008  0.678531  0.591333  0.428116  0.291997  0.438184  0.287482  \n",
       "1    1.186171  1.249968  1.136469  0.970300  0.862789  0.760773  0.660322  \n",
       "2    0.261463  0.527942  0.437047  0.640048  0.612196  0.687604  0.771689  \n",
       "3    0.337366  0.410842  0.129684  0.174816  0.183939  0.046147  0.115187  \n",
       "4    0.541897  0.702218  0.739326  0.678854  0.703749  0.676450  0.678985  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "652  0.666653  0.744708  0.703382  0.448111  0.470258  0.486729  0.387075  \n",
       "653  0.412837  0.553656  0.615626  0.667171  0.774697  0.795535  0.961883  \n",
       "654 -0.106552  0.009447 -0.046311 -0.038741 -0.131644  0.000624 -0.013170  \n",
       "655 -0.144815 -0.131977 -0.005740 -0.051106 -0.195408 -0.123638 -0.110192  \n",
       "656 -0.091053 -0.233653 -0.342134 -0.253696 -0.151273 -0.147495 -0.261391  \n",
       "\n",
       "[657 rows x 210 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "input_df = signal.detrend(input_df)\n",
    "scaler.fit(input_df)\n",
    "input_df = scaler.transform(input_df)\n",
    "input_df = pd.DataFrame(input_df)\n",
    "input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(657, 210)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = input_df.values\n",
    "y = target_df.values\n",
    "input_shape = X.shape\n",
    "input_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model\n",
    "model = models.Sequential()\n",
    "\n",
    "# Layer 1\n",
    "model.add(layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(210, 1)))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.ReLU())\n",
    "model.add(layers.AveragePooling1D(pool_size=2))\n",
    "\n",
    "# Layer 2\n",
    "model.add(layers.Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.ReLU())\n",
    "model.add(layers.AveragePooling1D(pool_size=2))\n",
    "\n",
    "# Layer 3\n",
    "model.add(layers.Conv1D(filters=256, kernel_size=3, activation='relu'))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.ReLU())\n",
    "\n",
    "# Layer 4\n",
    "model.add(layers.Conv1D(filters=256, kernel_size=3, activation='relu'))\n",
    "model.add(layers.BatchNormalization())\n",
    "model.add(layers.ReLU())\n",
    "model.add(layers.Dropout(0.5))  # You can adjust the dropout rat`e as needed\n",
    "\n",
    "# Layer 5\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(3, activation='linear'))  # 3 outputs for regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 208, 64)           256       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 208, 64)          256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " re_lu (ReLU)                (None, 208, 64)           0         \n",
      "                                                                 \n",
      " average_pooling1d (AverageP  (None, 104, 64)          0         \n",
      " ooling1D)                                                       \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 102, 128)          24704     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 102, 128)         512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_1 (ReLU)              (None, 102, 128)          0         \n",
      "                                                                 \n",
      " average_pooling1d_1 (Averag  (None, 51, 128)          0         \n",
      " ePooling1D)                                                     \n",
      "                                                                 \n",
      " conv1d_2 (Conv1D)           (None, 49, 256)           98560     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 49, 256)          1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_2 (ReLU)              (None, 49, 256)           0         \n",
      "                                                                 \n",
      " conv1d_3 (Conv1D)           (None, 47, 256)           196864    \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 47, 256)          1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_3 (ReLU)              (None, 47, 256)           0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 47, 256)           0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 12032)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 3)                 36099     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 359,299\n",
      "Trainable params: 357,891\n",
      "Non-trainable params: 1,408\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_learning_rate = 0.00001\n",
    "momentum = 0.3\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=base_learning_rate, momentum=momentum)\n",
    "\n",
    "# Compile the model (you can adjust the optimizer and loss as needed)\n",
    "model.compile(optimizer=optimizer, loss='mse', metrics=['accuracy'])\n",
    "\n",
    "# Print a summary of the model architecture\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33/33 [==============================] - 6s 48ms/step - loss: 6531.6514 - accuracy: 0.8743 - val_loss: 9301.0312 - val_accuracy: 1.0000\n",
      "Epoch 2/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 2160.0618 - accuracy: 0.9943 - val_loss: 9299.6777 - val_accuracy: 1.0000\n",
      "Epoch 3/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 693.9964 - accuracy: 0.9943 - val_loss: 9293.9219 - val_accuracy: 1.0000\n",
      "Epoch 4/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 369.5967 - accuracy: 0.9943 - val_loss: 9286.0400 - val_accuracy: 1.0000\n",
      "Epoch 5/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 278.0172 - accuracy: 0.9943 - val_loss: 9256.1641 - val_accuracy: 1.0000\n",
      "Epoch 6/50\n",
      "33/33 [==============================] - 0s 13ms/step - loss: 263.9877 - accuracy: 0.9943 - val_loss: 9216.3154 - val_accuracy: 1.0000\n",
      "Epoch 7/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 280.8322 - accuracy: 0.9943 - val_loss: 9125.7070 - val_accuracy: 1.0000\n",
      "Epoch 8/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 244.1761 - accuracy: 0.9943 - val_loss: 8922.7305 - val_accuracy: 1.0000\n",
      "Epoch 9/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 233.4356 - accuracy: 0.9943 - val_loss: 8805.7490 - val_accuracy: 1.0000\n",
      "Epoch 10/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 203.0148 - accuracy: 0.9943 - val_loss: 8743.8965 - val_accuracy: 1.0000\n",
      "Epoch 11/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 216.7086 - accuracy: 0.9943 - val_loss: 8120.7710 - val_accuracy: 1.0000\n",
      "Epoch 12/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 203.4666 - accuracy: 0.9943 - val_loss: 7540.4307 - val_accuracy: 1.0000\n",
      "Epoch 13/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 194.2502 - accuracy: 0.9943 - val_loss: 6878.2207 - val_accuracy: 1.0000\n",
      "Epoch 14/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 199.7363 - accuracy: 0.9943 - val_loss: 6661.5005 - val_accuracy: 1.0000\n",
      "Epoch 15/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 197.5078 - accuracy: 0.9943 - val_loss: 5420.6016 - val_accuracy: 1.0000\n",
      "Epoch 16/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 178.8827 - accuracy: 0.9943 - val_loss: 3323.3647 - val_accuracy: 1.0000\n",
      "Epoch 17/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 172.8024 - accuracy: 0.9943 - val_loss: 2721.2988 - val_accuracy: 1.0000\n",
      "Epoch 18/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 172.6437 - accuracy: 0.9943 - val_loss: 3197.2439 - val_accuracy: 1.0000\n",
      "Epoch 19/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 180.3621 - accuracy: 0.9943 - val_loss: 1922.8783 - val_accuracy: 1.0000\n",
      "Epoch 20/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 171.8486 - accuracy: 0.9943 - val_loss: 862.4279 - val_accuracy: 1.0000\n",
      "Epoch 21/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 162.4031 - accuracy: 0.9943 - val_loss: 417.1399 - val_accuracy: 1.0000\n",
      "Epoch 22/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 158.9898 - accuracy: 0.9943 - val_loss: 558.6718 - val_accuracy: 1.0000\n",
      "Epoch 23/50\n",
      "33/33 [==============================] - 0s 9ms/step - loss: 161.7417 - accuracy: 0.9943 - val_loss: 363.8768 - val_accuracy: 1.0000\n",
      "Epoch 24/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 161.0368 - accuracy: 0.9943 - val_loss: 901.8228 - val_accuracy: 1.0000\n",
      "Epoch 25/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 161.2824 - accuracy: 0.9943 - val_loss: 258.0434 - val_accuracy: 1.0000\n",
      "Epoch 26/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 146.9947 - accuracy: 0.9943 - val_loss: 285.0357 - val_accuracy: 1.0000\n",
      "Epoch 27/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 147.2729 - accuracy: 0.9943 - val_loss: 403.8548 - val_accuracy: 1.0000\n",
      "Epoch 28/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 148.3754 - accuracy: 0.9943 - val_loss: 300.6993 - val_accuracy: 1.0000\n",
      "Epoch 29/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 149.8072 - accuracy: 0.9943 - val_loss: 272.7307 - val_accuracy: 1.0000\n",
      "Epoch 30/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 135.7495 - accuracy: 0.9943 - val_loss: 492.2088 - val_accuracy: 1.0000\n",
      "Epoch 31/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 144.3763 - accuracy: 0.9943 - val_loss: 400.7716 - val_accuracy: 1.0000\n",
      "Epoch 32/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 141.4566 - accuracy: 0.9943 - val_loss: 268.6397 - val_accuracy: 1.0000\n",
      "Epoch 33/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 139.2762 - accuracy: 0.9943 - val_loss: 273.0535 - val_accuracy: 1.0000\n",
      "Epoch 34/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 144.6457 - accuracy: 0.9943 - val_loss: 486.7533 - val_accuracy: 1.0000\n",
      "Epoch 35/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 141.0497 - accuracy: 0.9943 - val_loss: 238.9115 - val_accuracy: 1.0000\n",
      "Epoch 36/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 126.3617 - accuracy: 0.9943 - val_loss: 293.4905 - val_accuracy: 1.0000\n",
      "Epoch 37/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 134.3267 - accuracy: 0.9943 - val_loss: 339.6118 - val_accuracy: 1.0000\n",
      "Epoch 38/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 130.3718 - accuracy: 0.9943 - val_loss: 278.1326 - val_accuracy: 1.0000\n",
      "Epoch 39/50\n",
      "33/33 [==============================] - 0s 12ms/step - loss: 123.6997 - accuracy: 0.9943 - val_loss: 312.9466 - val_accuracy: 1.0000\n",
      "Epoch 40/50\n",
      "33/33 [==============================] - 0s 11ms/step - loss: 126.8139 - accuracy: 0.9943 - val_loss: 251.0576 - val_accuracy: 1.0000\n",
      "Epoch 41/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 123.9796 - accuracy: 0.9943 - val_loss: 259.2406 - val_accuracy: 1.0000\n",
      "Epoch 42/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 122.2791 - accuracy: 0.9943 - val_loss: 358.8512 - val_accuracy: 1.0000\n",
      "Epoch 43/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 114.5659 - accuracy: 0.9943 - val_loss: 308.8540 - val_accuracy: 1.0000\n",
      "Epoch 44/50\n",
      "33/33 [==============================] - 0s 9ms/step - loss: 117.3410 - accuracy: 0.9943 - val_loss: 257.1988 - val_accuracy: 1.0000\n",
      "Epoch 45/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 115.7098 - accuracy: 0.9943 - val_loss: 427.7349 - val_accuracy: 1.0000\n",
      "Epoch 46/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 115.1749 - accuracy: 0.9943 - val_loss: 299.6535 - val_accuracy: 1.0000\n",
      "Epoch 47/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 112.1079 - accuracy: 0.9943 - val_loss: 271.9114 - val_accuracy: 1.0000\n",
      "Epoch 48/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 114.3911 - accuracy: 0.9943 - val_loss: 312.0117 - val_accuracy: 1.0000\n",
      "Epoch 49/50\n",
      "33/33 [==============================] - 0s 9ms/step - loss: 105.8034 - accuracy: 0.9943 - val_loss: 292.5039 - val_accuracy: 1.0000\n",
      "Epoch 50/50\n",
      "33/33 [==============================] - 0s 10ms/step - loss: 107.5829 - accuracy: 0.9943 - val_loss: 259.9575 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "epochs = 50  # You can adjust the number of epochs as needed\n",
    "batch_size = 16  # You can adjust the batch size as needed\n",
    "\n",
    "history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('Accuracy')\n",
    "# plt.ylim([min(plt.ylim()),1])\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('Cross Entropy')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "results = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[160,  77,  59],\n",
       "       [138,  69,  76],\n",
       "       [115,  62,  82],\n",
       "       [173, 107,  93],\n",
       "       [109,  68,  87],\n",
       "       [100,  63,  83],\n",
       "       [109,  64,  71],\n",
       "       [182,  80, 101],\n",
       "       [110,  75,  78],\n",
       "       [128,  79,  72],\n",
       "       [161,  89,  97],\n",
       "       [169,  90,  91],\n",
       "       [104,  70,  63],\n",
       "       [136,  64,  82],\n",
       "       [147,  79,  68],\n",
       "       [133,  72,  76],\n",
       "       [126,  78,  84],\n",
       "       [110,  75,  78],\n",
       "       [101,  63,  62],\n",
       "       [108,  65,  74],\n",
       "       [ 96,  67,  82],\n",
       "       [101,  70,  79],\n",
       "       [110,  63,  85],\n",
       "       [164,  73,  85],\n",
       "       [147,  70,  58],\n",
       "       [122,  77,  76],\n",
       "       [129,  64,  67],\n",
       "       [124,  85,  94],\n",
       "       [132,  62,  75],\n",
       "       [126,  78,  84],\n",
       "       [118,  71,  84],\n",
       "       [100,  71,  85],\n",
       "       [118,  71,  84],\n",
       "       [161,  89,  72],\n",
       "       [126,  65,  66],\n",
       "       [139,  85,  80],\n",
       "       [154,  88,  80],\n",
       "       [132,  62,  75],\n",
       "       [106,  69,  67],\n",
       "       [124,  79,  68],\n",
       "       [137,  80,  81],\n",
       "       [123,  62,  52],\n",
       "       [140,  82,  73],\n",
       "       [113,  55,  61],\n",
       "       [119,  65,  84],\n",
       "       [102,  66,  80],\n",
       "       [141,  67,  61],\n",
       "       [123,  78,  72],\n",
       "       [120,  63,  86],\n",
       "       [118,  71,  84],\n",
       "       [124,  70,  77],\n",
       "       [141,  72,  87],\n",
       "       [115,  64, 103],\n",
       "       [127,  83,  58],\n",
       "       [170,  87,  74],\n",
       "       [149, 101,  63],\n",
       "       [111,  65,  67],\n",
       "       [ 96,  67,  82],\n",
       "       [131,  72,  73],\n",
       "       [124,  79,  68],\n",
       "       [103,  65,  66],\n",
       "       [131,  79,  61],\n",
       "       [126,  65,  66],\n",
       "       [126,  63,  64],\n",
       "       [117,  70,  75],\n",
       "       [122,  73,  89],\n",
       "       [139,  75,  81],\n",
       "       [120,  60,  76],\n",
       "       [139,  69,  63],\n",
       "       [117,  82,  69],\n",
       "       [137,  80,  78],\n",
       "       [137,  74,  61],\n",
       "       [105,  70,  85],\n",
       "       [173,  90,  73],\n",
       "       [110,  63,  67],\n",
       "       [106,  53,  69],\n",
       "       [111,  65,  67],\n",
       "       [118,  55,  82],\n",
       "       [ 93,  57,  79],\n",
       "       [ 96,  67,  82],\n",
       "       [182,  80, 101],\n",
       "       [120,  69,  72],\n",
       "       [ 98,  59,  80],\n",
       "       [116,  65,  70],\n",
       "       [113,  62,  53],\n",
       "       [127,  65,  60],\n",
       "       [100,  55,  63],\n",
       "       [126,  65,  66],\n",
       "       [140,  72,  70],\n",
       "       [149,  70,  69],\n",
       "       [140,  87,  64],\n",
       "       [100,  63,  67],\n",
       "       [126,  63,  64],\n",
       "       [137,  65,  72],\n",
       "       [135,  69,  78],\n",
       "       [122,  65,  83],\n",
       "       [110,  63,  77],\n",
       "       [178,  86,  80],\n",
       "       [125,  74,  70],\n",
       "       [139,  75,  81],\n",
       "       [101,  63,  62],\n",
       "       [111,  62,  73],\n",
       "       [157,  94,  76],\n",
       "       [106,  53,  69],\n",
       "       [106,  63,  69],\n",
       "       [139,  84, 106],\n",
       "       [133,  76,  80],\n",
       "       [133,  53,  68],\n",
       "       [148,  69,  92],\n",
       "       [ 95,  57,  76],\n",
       "       [128,  64,  73],\n",
       "       [173, 100,  89],\n",
       "       [106,  69,  67],\n",
       "       [148,  88,  61],\n",
       "       [131,  74,  69],\n",
       "       [136,  93,  87],\n",
       "       [170,  87,  74],\n",
       "       [113,  62,  53],\n",
       "       [173, 107,  93],\n",
       "       [128,  77,  85],\n",
       "       [161,  89,  72],\n",
       "       [123,  56,  58],\n",
       "       [163,  63,  64],\n",
       "       [123,  62,  52],\n",
       "       [147,  85,  60],\n",
       "       [100,  71,  85],\n",
       "       [150,  96,  87],\n",
       "       [108,  73,  84],\n",
       "       [145,  82,  74],\n",
       "       [135,  63,  59],\n",
       "       [138,  69,  76],\n",
       "       [126,  81,  68]], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[41, 12,  5],\n",
       "       [ 6,  0,  1],\n",
       "       [26, 15,  0],\n",
       "       [60, 39, 25],\n",
       "       [21,  5,  2],\n",
       "       [ 3,  6, 14],\n",
       "       [ 0,  5, 13],\n",
       "       [51,  7, 22],\n",
       "       [12,  3,  6],\n",
       "       [20,  1,  8],\n",
       "       [10,  5,  8],\n",
       "       [44, 20, 18],\n",
       "       [10, 16,  8],\n",
       "       [18,  5, 11],\n",
       "       [19,  9,  3],\n",
       "       [ 7,  3,  4],\n",
       "       [ 8,  7, 12],\n",
       "       [ 3, 12, 12],\n",
       "       [13,  1,  2],\n",
       "       [10,  3,  2],\n",
       "       [30,  8,  6],\n",
       "       [ 1,  8, 15],\n",
       "       [ 7,  2, 13],\n",
       "       [34,  0,  7],\n",
       "       [33,  3,  3],\n",
       "       [ 8,  3,  1],\n",
       "       [ 6, 12, 12],\n",
       "       [16, 21, 25],\n",
       "       [14,  2,  2],\n",
       "       [21, 15, 23],\n",
       "       [32,  9,  7],\n",
       "       [ 3, 12, 19],\n",
       "       [22,  5,  4],\n",
       "       [32, 16,  2],\n",
       "       [13,  1,  1],\n",
       "       [22, 17, 12],\n",
       "       [37, 22, 15],\n",
       "       [ 7, 12,  5],\n",
       "       [ 6,  6,  4],\n",
       "       [16,  0,  8],\n",
       "       [11,  6,  1],\n",
       "       [ 7,  6, 13],\n",
       "       [34, 19,  7],\n",
       "       [23, 21, 14],\n",
       "       [23, 13,  3],\n",
       "       [13,  4,  0],\n",
       "       [23,  2,  4],\n",
       "       [ 4,  5,  0],\n",
       "       [ 3,  8, 12],\n",
       "       [13,  2,  8],\n",
       "       [ 2,  3,  2],\n",
       "       [24,  0, 12],\n",
       "       [23, 13, 19],\n",
       "       [ 9,  6, 12],\n",
       "       [38, 12,  2],\n",
       "       [20, 27,  2],\n",
       "       [ 2,  0,  3],\n",
       "       [37,  9, 11],\n",
       "       [16,  4,  4],\n",
       "       [12,  5,  6],\n",
       "       [ 7,  3,  3],\n",
       "       [15,  0, 23],\n",
       "       [ 9,  4,  6],\n",
       "       [ 7,  7,  6],\n",
       "       [ 7,  7, 12],\n",
       "       [ 0,  0, 17],\n",
       "       [ 3,  4,  2],\n",
       "       [13, 18,  1],\n",
       "       [ 9,  0,  4],\n",
       "       [ 6, 16,  6],\n",
       "       [ 3,  6,  3],\n",
       "       [ 2,  1,  6],\n",
       "       [12,  4, 13],\n",
       "       [60, 24,  8],\n",
       "       [32, 15, 10],\n",
       "       [24, 18,  5],\n",
       "       [14,  3,  1],\n",
       "       [ 5, 15,  8],\n",
       "       [20, 11,  1],\n",
       "       [24,  2,  4],\n",
       "       [42,  2, 19],\n",
       "       [ 5,  3,  0],\n",
       "       [13,  5, 12],\n",
       "       [ 7,  4,  4],\n",
       "       [ 1,  1,  6],\n",
       "       [10, 11, 17],\n",
       "       [ 5,  3,  6],\n",
       "       [ 3,  6,  8],\n",
       "       [ 8,  2,  2],\n",
       "       [31,  5, 11],\n",
       "       [20, 19,  0],\n",
       "       [19,  3,  1],\n",
       "       [12,  0,  2],\n",
       "       [15,  3,  4],\n",
       "       [ 5,  3,  2],\n",
       "       [32, 17,  3],\n",
       "       [ 0,  0,  7],\n",
       "       [45, 12,  4],\n",
       "       [13, 12,  1],\n",
       "       [21,  6,  9],\n",
       "       [ 8,  1,  2],\n",
       "       [ 6,  0, 10],\n",
       "       [38, 30,  6],\n",
       "       [20, 17,  3],\n",
       "       [ 6,  0,  6],\n",
       "       [17,  0, 22],\n",
       "       [ 0,  0,  2],\n",
       "       [13, 14,  2],\n",
       "       [12,  6, 18],\n",
       "       [39, 21,  7],\n",
       "       [ 4,  4,  3],\n",
       "       [38, 19,  5],\n",
       "       [10,  2,  0],\n",
       "       [33, 23,  2],\n",
       "       [ 0,  3,  1],\n",
       "       [17, 21, 17],\n",
       "       [37, 12,  2],\n",
       "       [ 3,  3,  8],\n",
       "       [57, 36, 19],\n",
       "       [ 8,  3,  8],\n",
       "       [43, 24,  7],\n",
       "       [ 7, 13, 10],\n",
       "       [27, 10,  6],\n",
       "       [ 2,  5, 12],\n",
       "       [35, 23,  1],\n",
       "       [ 3, 12, 19],\n",
       "       [47, 36, 26],\n",
       "       [14,  0,  9],\n",
       "       [41, 21, 14],\n",
       "       [13,  1,  3],\n",
       "       [13,  2,  5],\n",
       "       [20,  0, 16]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.abs(y_test - results)\n",
    "arr.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.12319286034657"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = tf.sqrt(tf.reduce_mean(tf.square(results - y_test)))\n",
    "rmse.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.366385712137667"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "rms = sqrt(mean_squared_error(y_test[2], results[2]))\n",
    "rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('fft_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'conv1d/kernel:0' shape=(3, 1, 64) dtype=float32, numpy=\n",
       " array([[[-0.01899141, -0.12895578,  0.00959825, -0.10220522,\n",
       "           0.15690239,  0.09120887, -0.02021323, -0.1816899 ,\n",
       "          -0.10901593,  0.14117952,  0.09277454, -0.08241797,\n",
       "           0.04754154, -0.1515262 , -0.01409099,  0.17471915,\n",
       "           0.09588616, -0.20095459,  0.11587352, -0.07678482,\n",
       "          -0.03757598, -0.03921561,  0.10369573,  0.10021866,\n",
       "          -0.16238612, -0.03702659, -0.00199702, -0.01472142,\n",
       "          -0.06639276, -0.09400446,  0.08079603,  0.14010905,\n",
       "           0.10044757, -0.01319007,  0.14026116,  0.17238927,\n",
       "          -0.012803  , -0.13816322, -0.10686425, -0.03066298,\n",
       "          -0.01381099,  0.00882818,  0.00357076,  0.15625283,\n",
       "           0.00085103, -0.17954479, -0.02134655, -0.06302365,\n",
       "          -0.14097112, -0.1050019 , -0.03725563, -0.03303744,\n",
       "          -0.01120301, -0.16918741,  0.151259  ,  0.07916596,\n",
       "          -0.14005582,  0.04400235, -0.04568806, -0.14742167,\n",
       "          -0.07834746,  0.1198677 , -0.10734325,  0.1264298 ]],\n",
       " \n",
       "        [[-0.06461354, -0.08562371,  0.13779539, -0.10435131,\n",
       "          -0.1775316 , -0.04309434, -0.16803749, -0.11290907,\n",
       "           0.03062621,  0.0714402 ,  0.1564485 ,  0.09263521,\n",
       "          -0.04294239, -0.02934549,  0.12129127, -0.0935569 ,\n",
       "          -0.12192392,  0.07597342, -0.08835418,  0.02564976,\n",
       "           0.02537136,  0.05465426,  0.01837569,  0.02627692,\n",
       "          -0.10562799, -0.02123625,  0.0882656 ,  0.01128838,\n",
       "          -0.1332365 ,  0.03731539, -0.06938905,  0.10837822,\n",
       "           0.12516148, -0.1371526 ,  0.15228848,  0.11225368,\n",
       "          -0.04510744, -0.09212605,  0.05271197,  0.14199096,\n",
       "          -0.10562073, -0.09892457,  0.0737385 ,  0.13887039,\n",
       "          -0.0154141 ,  0.05034284,  0.04385011, -0.08089437,\n",
       "           0.21604201, -0.1543426 ,  0.11876962,  0.00388424,\n",
       "           0.04410505,  0.02312418,  0.15688056,  0.13363566,\n",
       "          -0.13861346,  0.16083203, -0.17152974, -0.07092627,\n",
       "           0.06519211, -0.01171796,  0.18035695, -0.10653713]],\n",
       " \n",
       "        [[-0.10683157, -0.14501382,  0.01867779,  0.03848707,\n",
       "           0.09190751,  0.05477438, -0.03137111, -0.11727731,\n",
       "           0.01369042,  0.00854501,  0.09472375,  0.0642765 ,\n",
       "           0.17834207,  0.2067493 ,  0.02702268, -0.16642913,\n",
       "           0.10597456,  0.02903698,  0.13111931, -0.13447455,\n",
       "          -0.09823097,  0.06468396,  0.16290848,  0.00329207,\n",
       "           0.19600694, -0.14590399, -0.00676289,  0.11897974,\n",
       "          -0.17560752,  0.11933506, -0.16830513, -0.19178005,\n",
       "          -0.04386828, -0.02165287,  0.13143685, -0.03738628,\n",
       "          -0.07610956,  0.06258646, -0.12922494,  0.07545052,\n",
       "          -0.04091862, -0.08950035, -0.00590012,  0.10363891,\n",
       "          -0.15140945, -0.01948743,  0.10516357, -0.05945414,\n",
       "          -0.14348984, -0.00474013, -0.13108182, -0.07951032,\n",
       "           0.06172395, -0.10356123, -0.11436103,  0.14738521,\n",
       "           0.10144079,  0.0305582 ,  0.05006681,  0.02561212,\n",
       "           0.11226912, -0.00762907,  0.11145128, -0.12753446]]],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'conv1d/bias:0' shape=(64,) dtype=float32, numpy=\n",
       " array([-0.04718162,  0.01630137, -0.08146805, -0.10730883,  0.01528388,\n",
       "        -0.04379454, -0.04406346,  0.01095481,  0.12605162,  0.0166313 ,\n",
       "        -0.0337863 , -0.1438337 ,  0.00611037,  0.07435337, -0.05465088,\n",
       "         0.02133545, -0.01119126, -0.03952432,  0.02852239, -0.01386567,\n",
       "         0.0580861 ,  0.06905035,  0.0393423 , -0.12506768,  0.09147096,\n",
       "         0.01570317,  0.1177833 , -0.06985558,  0.02472874,  0.03816554,\n",
       "        -0.04445954,  0.08058439, -0.07673258, -0.17470108,  0.01078043,\n",
       "         0.01700992,  0.05118444,  0.0014111 , -0.05976385,  0.02643813,\n",
       "         0.01068013,  0.04890554,  0.11695109,  0.03874909, -0.0546488 ,\n",
       "         0.0338258 , -0.01879158,  0.00286769, -0.00733529,  0.02398513,\n",
       "         0.10130557, -0.00882761,  0.09949403, -0.00736044,  0.0431795 ,\n",
       "        -0.01870631,  0.00533594,  0.04242007,  0.01030746, -0.0169861 ,\n",
       "        -0.03647263,  0.07817259,  0.05320495,  0.07894519], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization/gamma:0' shape=(64,) dtype=float32, numpy=\n",
       " array([1.0001426 , 0.9933038 , 1.0058111 , 0.9985252 , 0.9988795 ,\n",
       "        0.9931546 , 0.99740934, 0.99951315, 1.0060571 , 1.0001554 ,\n",
       "        0.9973564 , 1.0006862 , 1.011847  , 0.99633145, 0.99357986,\n",
       "        0.9925494 , 0.99921733, 0.9991567 , 1.0020614 , 0.98919994,\n",
       "        0.99345297, 0.99746424, 0.994569  , 0.99626225, 1.0132699 ,\n",
       "        0.9969447 , 1.0043383 , 0.99219555, 0.99375325, 1.0016156 ,\n",
       "        0.99402773, 1.0002209 , 0.9956184 , 0.9966667 , 1.0012257 ,\n",
       "        0.9966948 , 0.98856145, 1.0000124 , 1.0009246 , 0.9986411 ,\n",
       "        0.99730295, 0.99813807, 0.9922304 , 0.997688  , 0.9954843 ,\n",
       "        0.9988139 , 1.0002166 , 0.9982625 , 1.0072808 , 1.0052096 ,\n",
       "        1.0045385 , 0.9958653 , 1.0050448 , 1.0086107 , 1.004224  ,\n",
       "        0.9987653 , 1.0044423 , 1.0088689 , 1.0106287 , 1.0026072 ,\n",
       "        0.995662  , 0.9877932 , 0.9923852 , 1.0029329 ], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization/beta:0' shape=(64,) dtype=float32, numpy=\n",
       " array([ 0.00457879,  0.01658247, -0.00323858, -0.01367336, -0.00374006,\n",
       "        -0.00932384, -0.00082303,  0.02206385,  0.06512735,  0.03395739,\n",
       "        -0.01221531, -0.00848307,  0.01548142,  0.02571989, -0.00878202,\n",
       "         0.00129561, -0.00624079,  0.00077478,  0.01732896, -0.01339469,\n",
       "         0.00500764,  0.00735424,  0.03128938, -0.01311511,  0.03760115,\n",
       "         0.01069128,  0.09478498, -0.00812473, -0.00907032,  0.0102271 ,\n",
       "        -0.0046973 ,  0.03254252, -0.01360703, -0.01417801,  0.01116033,\n",
       "         0.01905258,  0.01395497,  0.00187234, -0.00457186,  0.01560427,\n",
       "         0.00758698,  0.03780811,  0.06169979,  0.02063839, -0.00289412,\n",
       "         0.00847473, -0.00073775, -0.00334841, -0.00630227,  0.02923836,\n",
       "         0.04755821, -0.00676158,  0.03772449,  0.01151195,  0.0227099 ,\n",
       "        -0.00719609,  0.01062669,  0.03015339,  0.02800154,  0.00661539,\n",
       "        -0.00863618, -0.00324902,  0.02266728,  0.01445714], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization/moving_mean:0' shape=(64,) dtype=float32, numpy=\n",
       " array([0.04639132, 0.13779454, 0.03132552, 0.01710554, 0.03337813,\n",
       "        0.02419156, 0.05403796, 0.153712  , 0.12553738, 0.0868406 ,\n",
       "        0.11075077, 0.00310975, 0.06871561, 0.07408632, 0.03024979,\n",
       "        0.05017912, 0.02601303, 0.02601896, 0.07297017, 0.060219  ,\n",
       "        0.07885024, 0.07632615, 0.12198977, 0.0144742 , 0.09991448,\n",
       "        0.08286618, 0.11975852, 0.01909655, 0.14883688, 0.04797288,\n",
       "        0.03550287, 0.0853017 , 0.04132185, 0.01026266, 0.15781578,\n",
       "        0.09628412, 0.08193838, 0.06378048, 0.03585602, 0.08030353,\n",
       "        0.06702811, 0.09747565, 0.1171965 , 0.16058601, 0.03736299,\n",
       "        0.07200636, 0.03922885, 0.07393423, 0.02784411, 0.10851759,\n",
       "        0.10300137, 0.03031695, 0.10331228, 0.0850936 , 0.09330792,\n",
       "        0.12394536, 0.06907375, 0.1065209 , 0.06746113, 0.0606846 ,\n",
       "        0.02623546, 0.08681292, 0.09506875, 0.09529293], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization/moving_variance:0' shape=(64,) dtype=float32, numpy=\n",
       " array([0.0071402 , 0.0355625 , 0.00715947, 0.00257996, 0.00283749,\n",
       "        0.00356294, 0.00938958, 0.04578653, 0.00312937, 0.02367362,\n",
       "        0.04935301, 0.00042695, 0.01578978, 0.00276518, 0.00565623,\n",
       "        0.0029697 , 0.00261654, 0.00264051, 0.01387156, 0.00860505,\n",
       "        0.00516876, 0.00469528, 0.04153184, 0.00286278, 0.00534096,\n",
       "        0.0119841 , 0.00517807, 0.003162  , 0.0398767 , 0.00315282,\n",
       "        0.00461808, 0.00555267, 0.01063531, 0.00178041, 0.0836627 ,\n",
       "        0.02999442, 0.00700739, 0.00795934, 0.00545976, 0.0183303 ,\n",
       "        0.00753081, 0.01192586, 0.00437567, 0.07849899, 0.00528585,\n",
       "        0.00730992, 0.00678567, 0.01089264, 0.00175023, 0.02042482,\n",
       "        0.00258486, 0.00248101, 0.00744113, 0.01562698, 0.02148286,\n",
       "        0.05701838, 0.00920667, 0.02923338, 0.00849634, 0.00886729,\n",
       "        0.00387319, 0.00820424, 0.02003267, 0.00630131], dtype=float32)>,\n",
       " <tf.Variable 'conv1d_1/kernel:0' shape=(3, 64, 128) dtype=float32, numpy=\n",
       " array([[[ 0.06692192, -0.03192122, -0.08261016, ...,  0.05747599,\n",
       "           0.04062322, -0.04386819],\n",
       "         [-0.09911853,  0.02929512,  0.0578555 , ..., -0.09309393,\n",
       "           0.0051515 , -0.03684051],\n",
       "         [ 0.06855283,  0.06658431,  0.01736718, ..., -0.08955717,\n",
       "           0.01136964, -0.02731961],\n",
       "         ...,\n",
       "         [ 0.09371927,  0.06846198, -0.08003809, ...,  0.08671342,\n",
       "          -0.00785507,  0.04806069],\n",
       "         [-0.03701236,  0.06327069, -0.02632218, ...,  0.01969218,\n",
       "          -0.00819333,  0.02267511],\n",
       "         [-0.06546081, -0.0251312 ,  0.03265162, ..., -0.03717224,\n",
       "          -0.00042448,  0.08252531]],\n",
       " \n",
       "        [[ 0.01168535,  0.0482851 , -0.06697121, ..., -0.0288366 ,\n",
       "           0.01929035,  0.04966102],\n",
       "         [ 0.09921908, -0.0810577 , -0.0806358 , ...,  0.09393716,\n",
       "          -0.01443665,  0.01678819],\n",
       "         [ 0.02107354, -0.07569106, -0.02408667, ...,  0.03481009,\n",
       "          -0.04169438,  0.00421649],\n",
       "         ...,\n",
       "         [-0.0504762 ,  0.0818745 , -0.00257834, ...,  0.08911571,\n",
       "          -0.02650972, -0.0749539 ],\n",
       "         [-0.07861699,  0.08476172,  0.04475934, ...,  0.08969481,\n",
       "           0.07825465,  0.01878267],\n",
       "         [-0.03570296,  0.00857555, -0.03317136, ...,  0.08868163,\n",
       "           0.05947046,  0.02416459]],\n",
       " \n",
       "        [[-0.03518279,  0.05769286, -0.04382395, ..., -0.06781369,\n",
       "          -0.04754037,  0.02290869],\n",
       "         [ 0.04218169,  0.0225299 , -0.03262104, ...,  0.08021598,\n",
       "          -0.0481954 , -0.09129737],\n",
       "         [-0.08557165, -0.02487392,  0.08950888, ..., -0.07415275,\n",
       "          -0.06697185, -0.06096804],\n",
       "         ...,\n",
       "         [-0.02526367,  0.03691939,  0.00337564, ...,  0.06932306,\n",
       "          -0.01561361, -0.07591169],\n",
       "         [-0.0114337 , -0.01004625,  0.07268079, ..., -0.01148682,\n",
       "           0.04781224,  0.08511869],\n",
       "         [ 0.06598234,  0.06058202, -0.02783762, ..., -0.06090415,\n",
       "           0.05678259,  0.00432845]]], dtype=float32)>,\n",
       " <tf.Variable 'conv1d_1/bias:0' shape=(128,) dtype=float32, numpy=\n",
       " array([ 1.3897340e-03,  7.1560577e-02,  3.8421456e-02,  9.1335354e-03,\n",
       "         8.8474318e-02, -3.0081251e-03,  2.8417765e-03,  6.5864027e-02,\n",
       "         5.7636295e-03,  1.9696063e-01,  2.5778081e-02,  1.0075305e-04,\n",
       "         2.1667734e-02,  1.7372111e-02,  4.9966730e-02, -3.0184269e-03,\n",
       "         6.2800157e-03, -1.0216305e-02,  3.9266511e-03,  6.6244313e-03,\n",
       "         1.4130883e-03,  1.6104194e-03,  2.5807715e-03,  6.2042545e-03,\n",
       "        -2.3136553e-03,  8.9748371e-03,  7.5714979e-03,  1.4170218e-02,\n",
       "        -1.8188584e-03,  1.3073013e-03,  5.9501175e-03, -1.3976147e-03,\n",
       "         2.1409655e-03, -3.1513430e-04,  1.4765938e-01,  5.0101817e-02,\n",
       "         3.8651638e-03,  2.6625197e-02,  8.9049833e-03,  1.9076506e-02,\n",
       "        -6.1487796e-04,  8.8889414e-04, -6.4000292e-03,  1.6713348e-03,\n",
       "        -2.3840090e-02,  6.1667733e-02, -5.3193392e-03,  3.0726504e-03,\n",
       "         2.9887224e-03,  4.9642050e-03,  2.5654777e-03,  1.1267971e-01,\n",
       "        -1.2216745e-02,  1.3659297e-02,  9.3343526e-02,  1.0077078e-03,\n",
       "        -1.9278802e-04,  1.2560013e-01,  4.5802779e-03, -1.8532976e-04,\n",
       "         2.7457564e-03,  3.9097100e-02, -1.5186660e-02,  1.6241449e-01,\n",
       "         2.8246397e-02,  9.9814148e-04,  3.0358275e-04,  3.3269718e-03,\n",
       "        -4.6094539e-04,  2.2528684e-03, -4.3001422e-04, -5.7656126e-04,\n",
       "         1.3389050e-03, -1.5640371e-02,  3.8297661e-03,  8.3715282e-03,\n",
       "        -2.4276043e-03,  5.4021109e-02, -1.6060161e-03, -2.2816532e-03,\n",
       "         7.7501468e-02,  3.4410318e-03,  1.3890012e-02,  1.2595619e-02,\n",
       "        -3.1981422e-04,  5.7296553e-03,  5.1172320e-03, -1.0073349e-03,\n",
       "         3.4553248e-03,  1.3884099e-03,  3.1340534e-03,  9.6015062e-04,\n",
       "         7.5544968e-02,  3.0269439e-03,  2.1295575e-03,  2.2426832e-02,\n",
       "        -8.3540305e-03,  4.6356786e-02,  3.9952487e-02,  2.8162524e-03,\n",
       "         7.7301415e-04, -8.3597098e-04, -5.1757635e-04, -2.8022914e-03,\n",
       "         1.1750533e-01,  8.7288534e-04,  8.5320897e-02, -3.4787017e-04,\n",
       "         4.2945216e-03,  1.4970566e-03,  2.7754223e-03,  5.5868615e-04,\n",
       "         8.5420189e-03, -2.2358453e-04,  2.3991348e-02,  5.3310185e-04,\n",
       "        -7.3344854e-04,  3.3476045e-03, -2.0482721e-04,  2.1284146e-03,\n",
       "         8.5677700e-03,  5.3869020e-03,  5.4463878e-04, -8.0647442e-04,\n",
       "         5.2131386e-03,  5.8665103e-03,  7.0149653e-02,  7.7862732e-02],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_1/gamma:0' shape=(128,) dtype=float32, numpy=\n",
       " array([0.99416906, 0.998884  , 1.0037568 , 0.9958613 , 0.9908426 ,\n",
       "        0.9944462 , 0.9971848 , 1.0106095 , 0.9958642 , 1.0067159 ,\n",
       "        0.987773  , 1.0011836 , 0.9938495 , 1.000748  , 1.0133942 ,\n",
       "        0.9993116 , 0.99975175, 1.0006304 , 0.9982225 , 0.9969516 ,\n",
       "        0.99965775, 1.0063215 , 0.99680144, 1.0042089 , 1.0006112 ,\n",
       "        0.9953013 , 0.9970288 , 0.9999521 , 0.9950233 , 1.0006157 ,\n",
       "        0.99224406, 1.0004534 , 0.99750364, 0.9965696 , 1.0091115 ,\n",
       "        1.0031526 , 0.9961135 , 1.0042667 , 0.9975789 , 1.0016702 ,\n",
       "        0.9999742 , 0.99980164, 1.0130974 , 0.99961156, 1.0059053 ,\n",
       "        0.9983407 , 0.99748874, 0.9967585 , 0.99844605, 0.9984344 ,\n",
       "        1.0016023 , 1.0056026 , 0.9953539 , 0.99661213, 1.0046163 ,\n",
       "        1.0051402 , 1.0018489 , 0.9947583 , 1.0013658 , 0.99996096,\n",
       "        0.9973841 , 1.0037698 , 1.0017755 , 1.0085711 , 1.0173947 ,\n",
       "        0.99400085, 0.9991164 , 1.0009243 , 1.0043087 , 1.0009418 ,\n",
       "        1.0013243 , 1.0013477 , 0.99730307, 0.998733  , 1.0020471 ,\n",
       "        1.0055304 , 0.99531144, 0.9965539 , 1.0016074 , 0.9975273 ,\n",
       "        1.0048268 , 0.99751115, 0.99356127, 1.0016627 , 1.0071912 ,\n",
       "        0.99618983, 0.9925171 , 1.0003183 , 0.9980047 , 0.9999297 ,\n",
       "        0.9987051 , 0.996081  , 1.0005391 , 0.9988588 , 0.99779785,\n",
       "        1.0069538 , 1.0007535 , 1.0084109 , 0.99214363, 0.9953421 ,\n",
       "        0.9942583 , 1.0056418 , 1.0012326 , 1.0057125 , 1.0170876 ,\n",
       "        0.9931263 , 0.9996928 , 1.0028933 , 0.9994823 , 0.9973739 ,\n",
       "        0.99720913, 0.99835354, 0.9959661 , 0.9975624 , 1.0058914 ,\n",
       "        0.9994965 , 0.99634343, 0.9969533 , 1.0014238 , 0.99710745,\n",
       "        0.9944065 , 0.996609  , 0.99956685, 1.0064137 , 1.0009942 ,\n",
       "        1.0018753 , 1.013385  , 0.99536407], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_1/beta:0' shape=(128,) dtype=float32, numpy=\n",
       " array([-2.3472116e-03,  2.7314343e-03,  1.4196325e-02,  2.6236102e-03,\n",
       "         1.2134534e-02, -3.8543381e-03, -2.2346340e-03,  1.4175946e-02,\n",
       "         4.3125278e-03,  2.4115574e-02, -1.6674892e-03,  2.0884499e-03,\n",
       "         5.8587524e-03,  1.2037860e-02,  9.3636476e-03, -4.9301628e-03,\n",
       "         2.5931010e-03, -1.8738987e-03, -2.3110576e-04,  1.7279322e-03,\n",
       "         4.7884556e-03, -1.6426297e-03, -4.2485091e-04,  5.4845926e-03,\n",
       "        -3.7000321e-03,  5.3950045e-03,  5.3231348e-03,  5.9818113e-03,\n",
       "        -5.6677042e-03,  2.9907951e-03, -7.2528713e-04, -5.5100014e-03,\n",
       "         1.5475670e-03, -3.8412403e-04,  1.1684898e-02, -9.9622514e-05,\n",
       "         1.3788260e-03,  9.3331328e-03,  4.7067180e-03,  2.0622183e-03,\n",
       "         5.0152680e-03,  6.7577278e-04,  5.8361362e-03,  3.1158372e-03,\n",
       "        -1.4711397e-03,  4.6013896e-03, -4.2228214e-03, -3.6690020e-04,\n",
       "         3.5383743e-03,  3.3601485e-03,  4.6821907e-03,  1.4195449e-02,\n",
       "        -1.1916542e-03,  7.8912411e-04,  5.9277150e-03, -2.4667922e-03,\n",
       "         7.1968470e-04,  1.6883662e-02,  6.7393132e-03, -6.0258238e-03,\n",
       "        -7.3166692e-04,  5.2380995e-03, -3.4893532e-03,  1.9120729e-02,\n",
       "         5.0087543e-03, -3.4997638e-04,  2.0771057e-03,  1.9292205e-03,\n",
       "        -3.5348053e-03,  2.9308952e-03, -5.4060025e-03, -4.0778616e-03,\n",
       "         9.8760775e-04, -4.2758877e-03,  8.9218467e-03,  1.2695052e-02,\n",
       "        -5.2615241e-03,  5.8059297e-03, -6.3861487e-03, -7.5716926e-03,\n",
       "         1.4653065e-02,  3.2531116e-03,  1.7825076e-03,  6.7249783e-03,\n",
       "         3.9814124e-03,  2.9101914e-03, -1.3136135e-03, -1.9172549e-03,\n",
       "        -7.9769484e-04,  9.9791703e-04, -9.4539957e-04,  1.0220052e-03,\n",
       "         5.9778551e-03,  2.1552567e-03,  1.7091547e-04,  9.5795142e-03,\n",
       "        -3.2789777e-03,  7.4183550e-03,  5.0735362e-03,  1.3167673e-03,\n",
       "         2.4991268e-03, -6.6895117e-03,  3.0116551e-04, -1.2747081e-03,\n",
       "         1.6235838e-02, -2.3094078e-03,  4.2389641e-03,  2.8181188e-03,\n",
       "         5.4412712e-03,  4.9580884e-04,  8.0444233e-04, -6.7274930e-04,\n",
       "         3.8355973e-04,  1.7405337e-03,  3.3655320e-03,  2.5861354e-03,\n",
       "        -5.4949424e-03,  4.0198336e-03, -2.7147220e-03, -1.0782687e-03,\n",
       "        -1.8746469e-03,  3.1706630e-03,  5.1777186e-03,  8.4791500e-03,\n",
       "         5.7474421e-03,  7.4863513e-03,  4.6505732e-03,  5.1923175e-03],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_1/moving_mean:0' shape=(128,) dtype=float32, numpy=\n",
       " array([0.38272697, 0.03693196, 0.0148312 , 0.21777466, 0.04437253,\n",
       "        0.14406805, 0.16281605, 0.01787838, 0.2459091 , 0.06718826,\n",
       "        0.05811351, 0.6875549 , 0.09802917, 0.15363303, 0.04426237,\n",
       "        0.29356703, 0.2921784 , 0.08142499, 0.24080764, 0.0847816 ,\n",
       "        0.3721661 , 0.5195696 , 0.1852367 , 0.09150466, 0.34156948,\n",
       "        0.1911127 , 0.1995706 , 0.13440806, 0.1759427 , 0.25356635,\n",
       "        0.18772207, 0.52988386, 0.21861343, 0.30923706, 0.0450994 ,\n",
       "        0.01143053, 0.15022507, 0.31312144, 0.18476538, 0.04616863,\n",
       "        0.06786102, 0.43335205, 0.0711649 , 0.39994445, 0.06998578,\n",
       "        0.0194145 , 0.12775284, 0.15325828, 0.50118893, 0.21982272,\n",
       "        0.2184002 , 0.04026368, 0.0309009 , 0.01977402, 0.045194  ,\n",
       "        0.00509839, 0.18220013, 0.03595699, 0.22591482, 0.67897516,\n",
       "        0.15495302, 0.01284982, 0.03714266, 0.08523433, 0.04315925,\n",
       "        0.36200598, 0.47917244, 0.1696841 , 0.63760597, 0.0687571 ,\n",
       "        0.56332767, 0.54740655, 0.38261798, 0.00123718, 0.3523144 ,\n",
       "        0.2976896 , 0.25014782, 0.04673521, 0.3156471 , 0.39123523,\n",
       "        0.09378115, 0.25533965, 0.08844113, 0.13411957, 0.38367093,\n",
       "        0.23849688, 0.18517067, 0.35532492, 0.3624785 , 0.5538378 ,\n",
       "        0.14526364, 0.3247563 , 0.0491333 , 0.17596899, 0.32475945,\n",
       "        0.03422767, 0.01471842, 0.01056735, 0.03366121, 0.32974485,\n",
       "        0.48572367, 0.4706322 , 0.15090367, 0.1976321 , 0.05206154,\n",
       "        0.25826773, 0.06220202, 0.5106901 , 0.30658787, 0.13547294,\n",
       "        0.19884722, 0.1585034 , 0.10717317, 0.31688237, 0.00808082,\n",
       "        0.62430155, 0.24554847, 0.2423461 , 0.33767143, 0.09325927,\n",
       "        0.13441569, 0.19118328, 0.4352287 , 0.24476987, 0.18696791,\n",
       "        0.21988429, 0.00724659, 0.04919757], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_1/moving_variance:0' shape=(128,) dtype=float32, numpy=\n",
       " array([3.5935727e-01, 4.0768362e-03, 1.7595596e-03, 1.6749002e-01,\n",
       "        5.7674036e-03, 1.0419755e-01, 1.3016470e-01, 1.0510527e-03,\n",
       "        2.9120442e-01, 7.3992941e-03, 1.4772195e-02, 5.3890252e-01,\n",
       "        3.7029319e-02, 4.3715145e-02, 5.3869695e-03, 1.8968304e-01,\n",
       "        1.2335382e-01, 3.0203452e-02, 2.7425694e-01, 2.0294385e-02,\n",
       "        4.3747929e-01, 4.8040229e-01, 1.7856854e-01, 2.9602714e-02,\n",
       "        4.3457749e-01, 1.6766793e-01, 1.9103001e-01, 4.9324002e-02,\n",
       "        1.0195104e-01, 1.9471601e-01, 1.7358668e-01, 6.4327782e-01,\n",
       "        1.2465108e-01, 2.4860741e-01, 3.7338580e-03, 6.9130817e-04,\n",
       "        1.5207624e-01, 2.7768618e-01, 1.9224034e-01, 1.2244223e-02,\n",
       "        1.3908301e-02, 4.6921578e-01, 1.6728288e-02, 6.7287749e-01,\n",
       "        1.8624607e-02, 1.5227037e-03, 8.6622477e-02, 1.6169547e-01,\n",
       "        4.1184872e-01, 4.4953037e-02, 1.3290243e-01, 3.7566365e-03,\n",
       "        6.5701609e-03, 5.6800297e-03, 4.0274160e-03, 4.6004503e-04,\n",
       "        1.6443811e-01, 3.5019838e-03, 1.5035143e-01, 8.6508518e-01,\n",
       "        9.5123716e-02, 1.4881053e-03, 1.2604868e-02, 8.4521994e-03,\n",
       "        6.9880690e-03, 4.9614182e-01, 8.8789415e-01, 1.3394082e-01,\n",
       "        6.9254565e-01, 2.1262804e-02, 5.0416631e-01, 4.4600534e-01,\n",
       "        2.7476230e-01, 5.0138999e-05, 5.0073010e-01, 1.8133213e-01,\n",
       "        2.2418115e-01, 6.4693522e-03, 2.9923239e-01, 2.9478517e-01,\n",
       "        1.2669663e-02, 1.8140475e-01, 3.1503137e-02, 5.2610755e-02,\n",
       "        8.6782211e-01, 2.3608640e-01, 1.0666836e-01, 3.9634049e-01,\n",
       "        2.8701368e-01, 5.5094147e-01, 1.1353279e-01, 4.9945283e-01,\n",
       "        6.5518268e-03, 6.3726276e-02, 2.2929536e-01, 4.5849592e-03,\n",
       "        4.9009798e-03, 5.5333093e-04, 1.0573396e-02, 3.0075529e-01,\n",
       "        6.4546436e-01, 5.4813337e-01, 9.7002730e-02, 7.8983091e-02,\n",
       "        5.5495510e-03, 4.4503215e-01, 8.6779809e-03, 1.1177452e+00,\n",
       "        2.0508464e-01, 8.6265475e-02, 1.4265598e-01, 7.0723832e-02,\n",
       "        3.3158090e-02, 2.1838902e-01, 1.6065802e-03, 7.7345943e-01,\n",
       "        2.0506661e-01, 3.2920668e-01, 2.9545632e-01, 1.6131688e-02,\n",
       "        9.9130593e-02, 1.4052576e-01, 7.7610302e-01, 1.0484325e-01,\n",
       "        9.4951823e-02, 9.8299362e-02, 5.0605170e-04, 4.6504545e-03],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'conv1d_2/kernel:0' shape=(3, 128, 256) dtype=float32, numpy=\n",
       " array([[[ 0.05107092, -0.05278365, -0.01756229, ..., -0.07209233,\n",
       "           0.0486287 ,  0.02551308],\n",
       "         [ 0.00381921,  0.03268673, -0.01174242, ..., -0.05493369,\n",
       "          -0.06444468,  0.00577403],\n",
       "         [ 0.0369386 ,  0.02009162, -0.00343932, ...,  0.01765222,\n",
       "           0.01293814,  0.01323613],\n",
       "         ...,\n",
       "         [-0.00421111, -0.03125124, -0.0577292 , ...,  0.0500418 ,\n",
       "          -0.0292345 , -0.06667625],\n",
       "         [ 0.03854803, -0.02059895,  0.05925281, ...,  0.06758205,\n",
       "           0.05106068,  0.06955966],\n",
       "         [-0.03757215,  0.06797156, -0.01888247, ..., -0.0339212 ,\n",
       "           0.05087557, -0.03791456]],\n",
       " \n",
       "        [[-0.0285883 ,  0.05121104,  0.01390958, ...,  0.04584773,\n",
       "          -0.01014125,  0.02952592],\n",
       "         [ 0.05849508,  0.07276288,  0.02905854, ...,  0.04451273,\n",
       "           0.06299462,  0.01019676],\n",
       "         [ 0.00655947,  0.05334457,  0.03629592, ...,  0.05464832,\n",
       "          -0.04394272,  0.03802646],\n",
       "         ...,\n",
       "         [-0.04569254,  0.01321617, -0.01917915, ...,  0.0146234 ,\n",
       "          -0.02277887, -0.0246153 ],\n",
       "         [ 0.00326073, -0.02458926,  0.06441643, ..., -0.04232686,\n",
       "          -0.03407084, -0.0469748 ],\n",
       "         [-0.00968795,  0.05477604,  0.01098122, ..., -0.01675226,\n",
       "          -0.06227368, -0.05486798]],\n",
       " \n",
       "        [[-0.0664996 , -0.00060152,  0.02332003, ..., -0.07198863,\n",
       "          -0.02613304, -0.01002837],\n",
       "         [-0.05590169,  0.03574204,  0.0320278 , ..., -0.01615099,\n",
       "          -0.03687513, -0.06408847],\n",
       "         [-0.00175758, -0.04031873, -0.08696719, ...,  0.00962427,\n",
       "           0.05634826, -0.01483403],\n",
       "         ...,\n",
       "         [ 0.00245589,  0.03084006, -0.07576939, ...,  0.00719843,\n",
       "           0.05467007,  0.0067765 ],\n",
       "         [ 0.06930034, -0.00733946,  0.0563025 , ..., -0.0087253 ,\n",
       "          -0.0009908 , -0.05770457],\n",
       "         [ 0.07045332, -0.01706461,  0.02398769, ...,  0.042215  ,\n",
       "           0.02969304, -0.01482569]]], dtype=float32)>,\n",
       " <tf.Variable 'conv1d_2/bias:0' shape=(256,) dtype=float32, numpy=\n",
       " array([ 5.5538450e-04,  1.5995877e-03,  5.1603504e-03,  1.5370639e-03,\n",
       "         1.6824463e-03,  2.5008307e-03,  1.8277839e-04, -6.4226647e-04,\n",
       "         8.8672142e-04, -8.2841597e-04,  7.7102665e-04,  5.0265597e-05,\n",
       "         2.1043895e-03,  1.6608121e-03,  3.5105869e-03,  2.2135801e-03,\n",
       "        -4.6219715e-04,  8.2999887e-04,  5.0041807e-04,  3.2283094e-02,\n",
       "         2.2729485e-04,  1.3005777e-02,  3.6564651e-03,  1.0269947e-02,\n",
       "         2.3188224e-02,  2.8216054e-03,  3.7531897e-03, -3.8350450e-03,\n",
       "        -8.8513807e-05,  2.2647041e-03,  1.1058745e-03, -4.2947717e-03,\n",
       "        -2.7879959e-04, -2.3195781e-03, -2.4218758e-05,  1.6014563e-03,\n",
       "        -3.6873078e-04,  3.5436484e-03, -7.1452773e-04,  1.0638718e-04,\n",
       "         2.2176662e-02,  2.6489289e-02,  7.9034381e-03,  1.7070303e-02,\n",
       "         4.8377081e-03,  9.2695345e-04,  8.8162014e-05,  1.2974820e-02,\n",
       "        -5.6268479e-04,  1.3573408e-03,  7.5148987e-03,  4.2452584e-03,\n",
       "         3.0161946e-03,  6.0358428e-04, -1.3859560e-04,  5.9078336e-03,\n",
       "         1.0692440e-02,  7.2372914e-04,  4.6946129e-04,  2.3410041e-03,\n",
       "         1.2996200e-02,  1.4681725e-02, -5.5393763e-03,  6.5884124e-03,\n",
       "        -7.4132439e-04, -1.0571625e-03,  1.1627517e-04,  1.8460754e-02,\n",
       "         6.6043823e-03,  1.0491828e-02, -4.9429014e-04,  2.8084276e-02,\n",
       "         2.3858447e-03,  1.2772986e-02, -1.9937359e-04,  1.6080345e-04,\n",
       "         4.9813436e-03,  8.1461901e-03,  5.5091368e-04, -4.3043730e-04,\n",
       "         1.9429725e-02,  4.7958670e-03,  9.3184207e-03,  4.1831713e-04,\n",
       "         2.1529702e-02,  2.4125123e-04,  2.0771319e-04,  7.0684669e-03,\n",
       "        -8.6094821e-03,  2.0186675e-03,  2.0769185e-05,  3.4212545e-03,\n",
       "         9.2839161e-03,  2.6949255e-03,  3.6144883e-03, -2.5588351e-03,\n",
       "         1.8004503e-02, -1.9149561e-03,  4.1866330e-03,  9.6279435e-04,\n",
       "         1.1236312e-02,  2.2368366e-02,  7.8697409e-03,  5.5672601e-03,\n",
       "         3.5417630e-05,  3.8797923e-04, -1.1885215e-04,  5.8481202e-04,\n",
       "         1.0900522e-02,  8.2140509e-04,  1.2586483e-04,  3.1658777e-03,\n",
       "        -1.0858128e-04, -8.9017039e-06,  6.0883281e-03,  4.8250859e-03,\n",
       "         8.9200921e-03,  1.8576586e-03,  4.8553519e-04,  3.9790426e-03,\n",
       "         3.0644340e-04,  2.1804166e-03,  5.8111972e-03, -4.1250791e-03,\n",
       "         2.6040447e-03,  5.3058993e-03,  1.2256323e-02, -5.6522893e-04,\n",
       "        -5.2597099e-03,  1.0736725e-02,  1.9170414e-03,  1.3541569e-02,\n",
       "        -1.3676138e-03,  8.3322660e-04,  7.6080853e-04,  3.8541784e-03,\n",
       "         9.2790806e-04, -5.1891722e-04,  5.8111490e-04,  9.2083812e-03,\n",
       "        -5.0179841e-04, -3.0334301e-03,  1.7419379e-02,  5.9808150e-04,\n",
       "         2.1859195e-02, -4.0359708e-04,  4.7659720e-03,  3.9298180e-02,\n",
       "         1.3669906e-02,  1.4001009e-03, -2.7679887e-03,  5.2323020e-03,\n",
       "         3.1153443e-03,  8.0899004e-04,  6.1789458e-03,  1.9785279e-02,\n",
       "         1.6732387e-03, -2.2065966e-05,  7.5009838e-03,  2.3571192e-03,\n",
       "         3.0313886e-03,  7.8406818e-03, -3.7325756e-04,  2.3756064e-03,\n",
       "        -2.1049110e-03,  3.7230010e-04, -2.4477424e-04,  1.8136207e-03,\n",
       "         1.0212860e-02,  2.7121308e-03,  7.5985431e-03,  6.8787793e-03,\n",
       "         1.0748137e-02, -2.4963070e-03,  1.8576469e-04,  6.6799659e-04,\n",
       "         1.4665329e-02, -2.0034112e-04,  6.6009643e-03, -4.5820791e-04,\n",
       "         1.0082162e-02,  9.4891591e-03,  1.8741837e-02,  2.1562099e-02,\n",
       "         2.8376986e-04,  1.3392090e-04,  3.8963303e-03,  1.7136950e-02,\n",
       "        -6.9823425e-04,  6.3483156e-03,  1.6985276e-03,  2.5869941e-03,\n",
       "         1.6828801e-02, -1.8452333e-03,  6.6794422e-03,  1.1606129e-03,\n",
       "        -2.1628537e-03,  1.3558082e-04,  5.3009987e-03, -1.9272909e-03,\n",
       "         1.0773147e-02,  4.4322465e-04,  5.2520069e-03,  3.5688335e-03,\n",
       "        -1.4279629e-03,  1.2506431e-02,  1.1266713e-02,  2.3833023e-02,\n",
       "        -2.0183914e-03,  2.0176583e-04,  1.3259604e-02, -1.6745202e-03,\n",
       "        -3.8263394e-04, -3.3657516e-03, -2.2725243e-04, -4.3010516e-03,\n",
       "         5.9966198e-03,  5.1053576e-03,  1.2201457e-02, -6.6369964e-04,\n",
       "         1.3037703e-03,  3.0179417e-03,  7.4413163e-03,  6.6144374e-04,\n",
       "         2.9735374e-03, -1.1748952e-03, -1.6396778e-03,  1.9919004e-03,\n",
       "         1.4081483e-03, -1.0357966e-03, -3.3475351e-04,  6.4982346e-04,\n",
       "         1.1453405e-03,  4.4932896e-03,  7.3820126e-04,  3.6814101e-05,\n",
       "         3.5950111e-04,  1.8010830e-04, -2.1434086e-03,  2.7053838e-03,\n",
       "         2.8149845e-02,  1.0140114e-02, -1.7885332e-03,  7.5211836e-04,\n",
       "         1.3389517e-02,  1.9157217e-03,  9.3616964e-03,  8.1148269e-03,\n",
       "         2.6393882e-03,  6.4320760e-03,  1.4816853e-03,  1.7262225e-04,\n",
       "         4.6660495e-03,  3.1345244e-02,  2.2461645e-03,  1.1243098e-02],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_2/gamma:0' shape=(256,) dtype=float32, numpy=\n",
       " array([0.99676865, 0.99766046, 0.9998811 , 0.999436  , 0.994248  ,\n",
       "        0.99783224, 1.0026932 , 1.0000974 , 0.99908894, 0.9972836 ,\n",
       "        0.9973712 , 0.9954055 , 0.9958694 , 0.99830806, 0.998938  ,\n",
       "        0.9984467 , 1.0046904 , 0.9982886 , 0.9956124 , 1.0066669 ,\n",
       "        0.9978085 , 0.99889785, 0.99747175, 1.0061829 , 0.99480844,\n",
       "        1.0002643 , 0.99638104, 0.99907583, 0.99873   , 0.9976624 ,\n",
       "        0.9949695 , 1.0042303 , 1.0012143 , 1.0010049 , 0.9993894 ,\n",
       "        0.999141  , 1.0055896 , 0.9947775 , 1.0012054 , 0.99921685,\n",
       "        1.0032295 , 1.0089434 , 1.0037395 , 1.0063677 , 0.99779016,\n",
       "        1.0009904 , 1.001823  , 1.0075643 , 1.000196  , 0.9982134 ,\n",
       "        1.0011188 , 1.0006436 , 0.9979802 , 0.9988669 , 0.9990551 ,\n",
       "        1.0062451 , 0.9982559 , 1.0011655 , 0.9971147 , 1.000608  ,\n",
       "        0.99822426, 1.0002487 , 1.0014243 , 1.0020778 , 1.0046943 ,\n",
       "        1.0019776 , 1.000819  , 1.0008128 , 1.0003865 , 1.000838  ,\n",
       "        1.0015254 , 1.0096936 , 0.9977719 , 0.9988468 , 0.99945176,\n",
       "        0.99743944, 1.0016208 , 0.9956858 , 0.99943405, 1.0000329 ,\n",
       "        0.99563366, 0.99501187, 1.0008075 , 0.99821496, 1.0006262 ,\n",
       "        0.99919355, 1.004312  , 0.99928373, 1.0030046 , 0.99759656,\n",
       "        0.9979213 , 0.99427557, 1.0030067 , 1.0103105 , 0.9985478 ,\n",
       "        0.99607414, 1.0028131 , 1.0053266 , 0.99874485, 0.99879706,\n",
       "        0.99620914, 1.006428  , 1.0009431 , 1.001805  , 1.0011508 ,\n",
       "        1.0008019 , 0.9973473 , 1.0011934 , 1.0068719 , 0.99430966,\n",
       "        1.0017205 , 0.9976736 , 0.9987856 , 0.99839914, 1.0035481 ,\n",
       "        0.9984041 , 0.9998687 , 0.99826306, 1.001668  , 1.0010529 ,\n",
       "        0.9966739 , 0.9972801 , 0.9976167 , 0.9994873 , 1.0033413 ,\n",
       "        1.0004134 , 1.0044657 , 1.0043652 , 1.0033873 , 1.0023884 ,\n",
       "        1.0076944 , 1.0037787 , 1.0015081 , 1.0008148 , 1.0004741 ,\n",
       "        1.0027153 , 0.9997587 , 1.0009087 , 0.9979595 , 0.99723375,\n",
       "        1.0035663 , 1.0030582 , 1.0007517 , 0.9977727 , 1.0066085 ,\n",
       "        1.0029292 , 0.9970634 , 1.003998  , 0.999483  , 1.0013876 ,\n",
       "        1.0041635 , 0.9974852 , 0.9974287 , 0.9994695 , 1.0063708 ,\n",
       "        1.0071827 , 0.9973799 , 0.99965787, 0.99631506, 0.9975254 ,\n",
       "        0.99608994, 1.0027914 , 1.0012646 , 1.0021116 , 1.0008789 ,\n",
       "        0.9964308 , 0.999604  , 0.9957938 , 0.997738  , 0.99862826,\n",
       "        0.9993879 , 1.007053  , 1.002608  , 0.99885964, 0.9994244 ,\n",
       "        0.9973286 , 1.0017886 , 1.0002469 , 1.0005159 , 1.00231   ,\n",
       "        0.9999922 , 1.0012368 , 0.99713403, 1.0007725 , 0.99678165,\n",
       "        0.9997875 , 0.9970763 , 1.0012771 , 1.0014199 , 0.9959271 ,\n",
       "        0.99909776, 0.99907273, 1.0004051 , 1.0002131 , 1.0019114 ,\n",
       "        0.9957842 , 0.997653  , 0.9980326 , 1.0015676 , 1.0002639 ,\n",
       "        0.9958945 , 0.99569273, 1.0048699 , 0.9947849 , 0.9975441 ,\n",
       "        1.0009011 , 1.006789  , 1.0048745 , 1.002684  , 0.9987223 ,\n",
       "        1.0037413 , 1.0003781 , 1.0004501 , 1.0032085 , 1.0021236 ,\n",
       "        1.0021533 , 0.9936306 , 0.99655306, 1.0082991 , 0.99950916,\n",
       "        0.99701303, 0.99860305, 1.0004853 , 0.9978139 , 0.99573576,\n",
       "        0.9978728 , 0.999525  , 0.99461496, 0.9989998 , 0.9976056 ,\n",
       "        0.99978536, 0.998005  , 0.99825186, 1.0027045 , 1.0006994 ,\n",
       "        1.0030812 , 0.99879956, 1.0008279 , 1.0031136 , 0.99614185,\n",
       "        1.0104413 , 0.99856836, 0.9997723 , 0.9988911 , 0.998443  ,\n",
       "        1.002317  , 1.0047922 , 1.0001358 , 0.9936334 , 0.99665713,\n",
       "        1.0039717 , 1.0013424 , 1.000726  , 1.0026752 , 0.99682426,\n",
       "        1.0012772 ], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_2/beta:0' shape=(256,) dtype=float32, numpy=\n",
       " array([ 2.72024935e-03,  2.63461086e-04,  1.47584931e-03,  2.38613132e-03,\n",
       "         4.20142227e-04,  3.94420978e-03, -8.44776281e-04, -1.85253832e-03,\n",
       "         2.44690716e-04, -1.68912800e-03, -2.72348079e-05, -2.26760050e-03,\n",
       "         1.61854120e-03,  1.69187097e-03,  2.03614449e-03,  5.03209420e-04,\n",
       "        -4.37738746e-03,  4.22250218e-04,  1.90985994e-03,  1.15084443e-02,\n",
       "        -6.31724251e-04,  2.05893139e-03,  2.13118317e-03,  1.09216245e-02,\n",
       "         3.42969038e-03,  1.12739904e-03,  1.78233173e-03, -8.06745898e-04,\n",
       "        -2.44928931e-04,  3.00004939e-03, -1.57962670e-03,  3.12328106e-04,\n",
       "         1.20199446e-04,  3.38188751e-04,  1.13394999e-04,  1.00212509e-03,\n",
       "         3.67420987e-04,  1.88186776e-03, -2.29317346e-03,  2.48679169e-03,\n",
       "         8.48248135e-03,  6.95556542e-03,  3.37561779e-03,  6.12512790e-03,\n",
       "         2.82772584e-03,  2.74662068e-03,  1.60862319e-03,  8.28299299e-03,\n",
       "        -1.41689833e-03,  1.41862419e-03,  2.16788449e-03,  2.77863909e-03,\n",
       "         2.08310946e-03,  1.05524913e-03, -6.06759626e-04,  5.42722316e-03,\n",
       "         1.87233894e-03,  2.00330513e-03,  1.16140523e-03,  2.39254581e-03,\n",
       "         4.28007776e-03,  8.86008423e-03, -4.49634390e-03,  5.42617543e-03,\n",
       "        -2.31494871e-03, -2.52215751e-03, -1.16512564e-03,  7.28553114e-03,\n",
       "         1.74628245e-03,  2.76193512e-03,  1.45080942e-03,  5.37056150e-03,\n",
       "        -5.30703808e-04,  2.50106654e-03, -1.27442327e-04, -7.73481675e-04,\n",
       "         3.46005359e-03,  2.53520231e-03,  1.36652449e-03, -3.27130710e-03,\n",
       "         2.60038860e-03, -2.06592740e-04,  6.16722833e-03,  1.01894504e-04,\n",
       "         4.22994327e-03, -6.67203625e-04,  2.96245818e-03,  5.35294786e-03,\n",
       "        -3.67328117e-04,  2.82150554e-03, -2.43117264e-03,  2.83743348e-03,\n",
       "         4.64784540e-03,  3.72263044e-03,  2.13801302e-03, -4.22885222e-03,\n",
       "         5.72594721e-03, -7.96878943e-04,  1.93489576e-03,  4.51168635e-05,\n",
       "         4.89632972e-03,  8.51320289e-03,  3.08875320e-03,  4.48875735e-03,\n",
       "         6.36334938e-04, -1.23153254e-03, -6.10620482e-04,  3.16736242e-03,\n",
       "         3.74591863e-03,  8.42872032e-06,  2.51968484e-03,  3.38703324e-03,\n",
       "        -7.51870393e-04,  4.22810641e-04,  4.56401194e-03,  1.68379571e-03,\n",
       "         5.75594045e-03,  2.17732065e-03,  1.68871915e-03,  3.58125218e-03,\n",
       "        -1.22856617e-03, -2.75235507e-04,  5.67048788e-04, -3.07991216e-03,\n",
       "         2.79409112e-03,  3.74216586e-03,  8.88815336e-03,  2.03472772e-03,\n",
       "        -3.95835005e-03,  2.38983030e-03,  9.13042016e-03,  4.77560190e-03,\n",
       "         5.32911450e-04,  2.37217231e-04,  1.82924978e-03,  2.28110328e-03,\n",
       "         2.05687946e-03,  2.10431957e-04,  1.48972799e-03,  1.70026289e-03,\n",
       "        -2.84842425e-03,  1.63202093e-03,  8.47552810e-03, -1.06768555e-03,\n",
       "         1.24004446e-02, -2.26219906e-03,  1.27640041e-03,  5.72861591e-03,\n",
       "         3.62558546e-03, -7.95027707e-04, -4.87916498e-03,  1.29426143e-03,\n",
       "         1.27711019e-03,  3.71343398e-04,  9.44005419e-03,  8.26097559e-03,\n",
       "         9.86771192e-04,  2.59287219e-04,  1.09187502e-03,  4.03222395e-03,\n",
       "         3.11974529e-03,  7.10723689e-03, -1.66094932e-03,  4.43372875e-03,\n",
       "         6.16796664e-04, -9.93763679e-04, -3.20733234e-04, -7.28940358e-04,\n",
       "         1.82589551e-03,  5.47329523e-03,  8.97466205e-03,  3.98728531e-03,\n",
       "         2.95391516e-03, -1.87560718e-03,  4.99940594e-04, -1.66062033e-03,\n",
       "         5.45487786e-03, -2.22411193e-03,  2.42066756e-03, -2.58368975e-03,\n",
       "         3.67868412e-03,  4.53243451e-03,  4.21945658e-03,  6.64790021e-03,\n",
       "        -1.22006983e-03,  2.15354434e-04,  1.88582262e-03,  3.87099735e-03,\n",
       "        -6.36982091e-04,  5.75661252e-04,  6.46926172e-04,  3.86716705e-03,\n",
       "         1.01787988e-02, -2.39086221e-03,  1.12597421e-02,  1.56638317e-03,\n",
       "        -4.31045564e-03, -5.97075792e-04,  2.28164159e-03, -1.89256645e-03,\n",
       "         4.71228588e-04,  3.27079506e-05,  5.02793305e-03,  7.92446954e-04,\n",
       "        -1.73110992e-03,  3.16554215e-03,  8.21725372e-03,  5.39145991e-03,\n",
       "         7.71029328e-04, -2.93772289e-04,  3.01198009e-03, -1.26304443e-03,\n",
       "         6.31546602e-04,  3.75644397e-03,  1.23759184e-03, -1.29852828e-03,\n",
       "        -4.23562597e-05,  3.40316328e-03,  1.27670243e-02, -3.01468675e-03,\n",
       "        -1.80724275e-03,  1.09771010e-03,  5.34578925e-03,  2.96480558e-03,\n",
       "         2.43252260e-03, -2.33335770e-03, -1.08634098e-03,  2.79508485e-03,\n",
       "         3.98240052e-04, -1.59631507e-03,  9.64812178e-04, -9.32460884e-04,\n",
       "         1.34221278e-03,  3.77987814e-03,  1.83452643e-03,  1.64020783e-03,\n",
       "         1.13112456e-03,  1.04976387e-03, -5.45894122e-03, -1.31599710e-03,\n",
       "         9.76088364e-03,  9.48806293e-04, -3.17273196e-03, -1.15702620e-04,\n",
       "         9.08769015e-03,  3.49528342e-03,  5.49295032e-03,  4.13192343e-03,\n",
       "         3.39212641e-03,  9.59480414e-04,  2.92940717e-03,  3.84894112e-04,\n",
       "         4.90889372e-03,  7.38921762e-03,  8.04681680e-04,  1.02798166e-02],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_2/moving_mean:0' shape=(256,) dtype=float32, numpy=\n",
       " array([0.3774086 , 0.2353218 , 0.05262908, 0.2838753 , 0.21106207,\n",
       "        0.4084689 , 0.82779276, 0.3271583 , 0.24511364, 0.11968801,\n",
       "        0.47110707, 0.298892  , 0.18237409, 0.2774178 , 0.12751594,\n",
       "        0.07231431, 0.4930714 , 0.21823356, 0.3095904 , 0.10786971,\n",
       "        0.38046312, 0.10431421, 0.2133021 , 0.08568293, 0.09271515,\n",
       "        0.11885095, 0.23621254, 0.04184244, 0.35316664, 0.19766013,\n",
       "        0.31240505, 0.2587015 , 0.46589538, 0.18450472, 0.44080487,\n",
       "        0.21738681, 0.71665525, 0.29103693, 0.38810065, 0.93543774,\n",
       "        0.11847442, 0.01642766, 0.04407484, 0.06107885, 0.3147778 ,\n",
       "        0.21493278, 0.67446494, 0.08187339, 0.34092104, 0.4741947 ,\n",
       "        0.05411335, 0.11358266, 0.19499542, 0.23423348, 0.35901394,\n",
       "        0.11458706, 0.14408456, 0.34249747, 0.61809695, 0.11522968,\n",
       "        0.1773605 , 0.20309019, 0.20637678, 0.1498106 , 0.37465003,\n",
       "        0.15311281, 0.2308118 , 0.03590416, 0.00229359, 0.0100147 ,\n",
       "        0.214681  , 0.01410999, 0.19457294, 0.12454913, 0.15698887,\n",
       "        0.2667002 , 0.0248338 , 0.08397421, 0.3435167 , 0.40033332,\n",
       "        0.06992903, 0.19675799, 0.16938859, 0.19429821, 0.07229497,\n",
       "        0.4495828 , 0.01114991, 0.16418453, 0.01612548, 0.28927433,\n",
       "        0.28911665, 0.2686695 , 0.11278873, 0.00438195, 0.21600348,\n",
       "        0.13808511, 0.02166593, 0.42411795, 0.19870095, 0.2146326 ,\n",
       "        0.13429251, 0.01776298, 0.083955  , 0.15613283, 0.6483817 ,\n",
       "        0.46388748, 0.30377898, 0.5354268 , 0.01548406, 0.32164466,\n",
       "        0.31835932, 0.14424004, 0.43980858, 0.42746696, 0.0879069 ,\n",
       "        0.1911521 , 0.17021716, 0.36624622, 0.19915208, 0.22588612,\n",
       "        0.43587255, 0.17558603, 0.07884177, 0.21427326, 0.01385575,\n",
       "        0.11815356, 0.10158494, 0.15110789, 0.2936592 , 0.01938752,\n",
       "        0.1021155 , 0.0905934 , 0.10898246, 0.41076148, 0.47978637,\n",
       "        0.04267534, 0.17754805, 0.10057856, 0.4374377 , 0.03008116,\n",
       "        0.5955846 , 0.06352329, 0.02793211, 0.362711  , 0.13853957,\n",
       "        0.54012346, 0.23221573, 0.02624489, 0.09455959, 0.4109765 ,\n",
       "        0.40085283, 0.2112977 , 0.21750726, 0.22574091, 0.05221326,\n",
       "        0.06730884, 0.2031672 , 0.21492541, 0.16692552, 0.2329192 ,\n",
       "        0.28438947, 0.23050632, 0.36092767, 0.13519603, 0.10486437,\n",
       "        0.4697266 , 0.5864346 , 0.24251014, 0.1098907 , 0.32234442,\n",
       "        0.2936684 , 0.01682012, 0.07394759, 0.22877727, 0.67028993,\n",
       "        0.45644552, 0.07502186, 0.5567928 , 0.05037718, 0.61089957,\n",
       "        0.05609728, 0.0938184 , 0.06180434, 0.08513471, 0.25287533,\n",
       "        0.4538698 , 0.12730952, 0.03182106, 0.15407538, 0.14906214,\n",
       "        0.20401177, 0.19976711, 0.17456771, 0.32554734, 0.2601733 ,\n",
       "        0.39934456, 0.25365123, 0.5245336 , 0.06301607, 0.20760745,\n",
       "        0.1871415 , 0.2601199 , 0.21047725, 0.24177964, 0.21134147,\n",
       "        0.04856878, 0.06115416, 0.01232252, 0.33242437, 0.28012225,\n",
       "        0.02261345, 0.16259538, 0.52377623, 0.04694235, 0.5898877 ,\n",
       "        0.20956612, 0.02123826, 0.12640105, 0.11209708, 0.37964246,\n",
       "        0.3834185 , 0.09958363, 0.18609333, 0.31722963, 0.18432398,\n",
       "        0.24478455, 0.14499864, 0.34956336, 0.12442592, 0.14576696,\n",
       "        0.2512922 , 0.42291465, 0.32438466, 0.11277933, 0.45408458,\n",
       "        0.51146793, 0.5520401 , 0.40234873, 0.45039517, 0.1449881 ,\n",
       "        0.01564202, 0.00915834, 0.30513   , 0.30371702, 0.19232072,\n",
       "        0.19640954, 0.09964211, 0.11637497, 0.33270168, 0.15766262,\n",
       "        0.11494549, 0.49241155, 0.20269011, 0.09466524, 0.21172722,\n",
       "        0.1468453 ], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_2/moving_variance:0' shape=(256,) dtype=float32, numpy=\n",
       " array([3.23012173e-01, 1.55995518e-01, 1.36607001e-02, 1.05582468e-01,\n",
       "        1.22594431e-01, 4.19399023e-01, 1.35594714e+00, 3.54546905e-01,\n",
       "        3.44757527e-01, 1.33584574e-01, 6.26119554e-01, 2.24345997e-01,\n",
       "        1.21122770e-01, 3.19583327e-01, 1.10302195e-01, 1.88537259e-02,\n",
       "        3.62008482e-01, 3.47395867e-01, 3.05099905e-01, 3.43127139e-02,\n",
       "        4.34107363e-01, 5.81602305e-02, 2.02329263e-01, 1.90531109e-02,\n",
       "        3.91302370e-02, 4.20455858e-02, 2.41481140e-01, 1.00248540e-02,\n",
       "        1.77775055e-01, 1.27867311e-01, 3.35792363e-01, 6.03258722e-02,\n",
       "        5.14300227e-01, 7.43420720e-02, 3.68525743e-01, 1.28987446e-01,\n",
       "        2.09011644e-01, 2.67111868e-01, 8.71130303e-02, 1.65083289e+00,\n",
       "        4.20227125e-02, 4.12776042e-03, 9.75614693e-03, 1.61149930e-02,\n",
       "        1.07694916e-01, 2.02470511e-01, 1.00945187e+00, 6.36354759e-02,\n",
       "        3.15841496e-01, 3.56467694e-01, 1.59607995e-02, 3.61759812e-02,\n",
       "        1.10784635e-01, 3.70756835e-01, 4.63030368e-01, 8.97535905e-02,\n",
       "        1.10164888e-01, 3.81362230e-01, 4.57881033e-01, 5.79207912e-02,\n",
       "        3.99127379e-02, 8.19653422e-02, 5.99491149e-02, 1.06310219e-01,\n",
       "        1.12668306e-01, 7.31126517e-02, 1.07234664e-01, 8.94140732e-03,\n",
       "        4.30888031e-04, 1.43030344e-03, 2.01812640e-01, 2.78169964e-03,\n",
       "        6.97032958e-02, 2.89615598e-02, 6.64675161e-02, 1.54750422e-01,\n",
       "        8.51393025e-03, 2.89850682e-02, 3.56104940e-01, 4.09539938e-01,\n",
       "        2.31015533e-02, 2.08881229e-01, 4.08782586e-02, 2.92940855e-01,\n",
       "        1.72633380e-02, 7.42760539e-01, 2.16153939e-03, 4.73739542e-02,\n",
       "        6.18867623e-03, 1.36976644e-01, 3.75959277e-01, 2.71111220e-01,\n",
       "        3.92580330e-02, 7.22681580e-04, 9.79697779e-02, 4.30870689e-02,\n",
       "        4.53576306e-03, 1.85241908e-01, 1.02812767e-01, 1.30521730e-01,\n",
       "        7.34550580e-02, 2.74410611e-03, 6.32486567e-02, 1.14354126e-01,\n",
       "        2.02709258e-01, 4.15063113e-01, 3.46484095e-01, 5.98196387e-01,\n",
       "        3.02121439e-03, 4.24409926e-01, 1.56260625e-01, 1.62577018e-01,\n",
       "        2.88519084e-01, 2.27512419e-01, 2.78681219e-02, 5.50331622e-02,\n",
       "        5.99329732e-02, 1.56258807e-01, 1.92708313e-01, 1.79628268e-01,\n",
       "        3.30729246e-01, 1.17479518e-01, 5.48727103e-02, 9.99844968e-02,\n",
       "        3.09447083e-03, 3.49509008e-02, 2.64383107e-02, 4.53133434e-02,\n",
       "        1.21237017e-01, 3.63081135e-03, 2.23967284e-02, 3.02363690e-02,\n",
       "        3.56415622e-02, 3.99522871e-01, 3.69430363e-01, 9.86246765e-03,\n",
       "        1.38577998e-01, 9.02298689e-02, 4.75622267e-01, 1.39637105e-02,\n",
       "        9.11668718e-01, 2.16457304e-02, 4.97601554e-03, 1.27335399e-01,\n",
       "        4.45836447e-02, 1.43119007e-01, 6.78891242e-02, 5.24390303e-03,\n",
       "        2.36087497e-02, 2.47679293e-01, 3.69056106e-01, 5.63810505e-02,\n",
       "        7.94831291e-02, 2.06459701e-01, 1.22259390e-02, 2.09413245e-02,\n",
       "        1.21158734e-01, 1.27939865e-01, 7.79362097e-02, 1.99029818e-01,\n",
       "        2.58229673e-01, 2.96569973e-01, 1.30791873e-01, 4.90731075e-02,\n",
       "        8.02656636e-02, 4.62133616e-01, 9.09309864e-01, 1.52700379e-01,\n",
       "        2.55650226e-02, 2.14623868e-01, 1.04313016e-01, 2.84235948e-03,\n",
       "        2.03719009e-02, 1.76573575e-01, 2.89983243e-01, 3.47590894e-01,\n",
       "        1.71392318e-02, 6.52061880e-01, 2.04012115e-02, 6.83342576e-01,\n",
       "        2.67790612e-02, 2.85059176e-02, 1.47603014e-02, 2.08794679e-02,\n",
       "        2.55574942e-01, 2.11250156e-01, 5.31694889e-02, 6.70094928e-03,\n",
       "        1.73263609e-01, 6.83233216e-02, 8.71621892e-02, 1.16409846e-01,\n",
       "        6.57929927e-02, 1.39656425e-01, 9.47872847e-02, 3.90248090e-01,\n",
       "        8.66521522e-02, 3.78346622e-01, 1.86768472e-02, 8.58145803e-02,\n",
       "        4.41712178e-02, 2.56748199e-01, 8.51858407e-02, 1.52827486e-01,\n",
       "        4.37222093e-01, 1.70007255e-02, 1.32885464e-02, 2.58624111e-03,\n",
       "        6.20479509e-02, 1.35539219e-01, 4.17621620e-03, 1.79729626e-01,\n",
       "        4.37366247e-01, 8.22818652e-03, 1.19420111e-01, 8.67729932e-02,\n",
       "        1.16115259e-02, 1.31609693e-01, 2.72237249e-02, 3.74671876e-01,\n",
       "        2.10442290e-01, 1.07441589e-01, 7.22570792e-02, 2.06102267e-01,\n",
       "        4.88430671e-02, 1.36265218e-01, 1.01106651e-01, 4.96575326e-01,\n",
       "        8.28617811e-02, 1.01956092e-01, 1.30675569e-01, 5.89644074e-01,\n",
       "        3.67885262e-01, 3.18730175e-02, 3.50658178e-01, 1.36688739e-01,\n",
       "        2.52495974e-01, 5.77811539e-01, 6.23617232e-01, 5.38388789e-02,\n",
       "        2.63565569e-03, 2.82274373e-03, 3.19194466e-01, 1.21561117e-01,\n",
       "        7.48283416e-02, 4.88427617e-02, 4.70666960e-02, 7.36229271e-02,\n",
       "        5.65244138e-01, 5.37446514e-02, 4.59516495e-02, 7.16445088e-01,\n",
       "        1.93713933e-01, 2.52003502e-02, 9.85515714e-02, 3.70801985e-02],\n",
       "       dtype=float32)>,\n",
       " <tf.Variable 'conv1d_3/kernel:0' shape=(3, 256, 256) dtype=float32, numpy=\n",
       " array([[[-0.04747128,  0.00873623, -0.0242069 , ...,  0.04202365,\n",
       "          -0.0399003 ,  0.00990781],\n",
       "         [ 0.05812587,  0.05525907,  0.02791808, ..., -0.05228646,\n",
       "          -0.05975047,  0.02836463],\n",
       "         [ 0.05360609,  0.00231563, -0.03374267, ...,  0.01476821,\n",
       "          -0.0460237 , -0.00996919],\n",
       "         ...,\n",
       "         [ 0.06071718,  0.03782638,  0.02051964, ..., -0.05162613,\n",
       "           0.01187162,  0.02531475],\n",
       "         [ 0.02540663, -0.05800826, -0.01486797, ...,  0.03249516,\n",
       "           0.00495732,  0.04162383],\n",
       "         [ 0.05525479, -0.05298634,  0.01264792, ...,  0.02650063,\n",
       "           0.05372024, -0.03025728]],\n",
       " \n",
       "        [[-0.05327312,  0.04586342, -0.04222654, ...,  0.05471233,\n",
       "          -0.0452936 , -0.02313551],\n",
       "         [ 0.04030368,  0.02710439,  0.05712309, ...,  0.03132175,\n",
       "          -0.04742167,  0.0608209 ],\n",
       "         [ 0.01174826, -0.00105763, -0.01665969, ..., -0.02707522,\n",
       "           0.0492651 ,  0.02572375],\n",
       "         ...,\n",
       "         [ 0.00343057,  0.05396754,  0.0453826 , ...,  0.04175648,\n",
       "           0.00255046,  0.00546616],\n",
       "         [-0.03969799, -0.01535491,  0.05489518, ..., -0.01184394,\n",
       "           0.05465177, -0.00255571],\n",
       "         [ 0.05509249,  0.00745005,  0.04600044, ..., -0.02542792,\n",
       "           0.06217467,  0.04555061]],\n",
       " \n",
       "        [[ 0.03027337, -0.02203069, -0.02552411, ..., -0.05076099,\n",
       "           0.0337113 , -0.05526648],\n",
       "         [-0.03745527,  0.03738558, -0.0343795 , ...,  0.05605199,\n",
       "           0.05487432,  0.0564907 ],\n",
       "         [ 0.01432042, -0.02987196,  0.02783671, ...,  0.05663571,\n",
       "          -0.04651062, -0.02530456],\n",
       "         ...,\n",
       "         [ 0.05217143,  0.04108591, -0.00661513, ..., -0.04600645,\n",
       "           0.05966767,  0.00269189],\n",
       "         [ 0.04795146, -0.03827735, -0.03258201, ..., -0.01480087,\n",
       "           0.01236554,  0.04074387],\n",
       "         [-0.02622375, -0.00725033,  0.06058349, ..., -0.0151138 ,\n",
       "           0.00561538,  0.04963401]]], dtype=float32)>,\n",
       " <tf.Variable 'conv1d_3/bias:0' shape=(256,) dtype=float32, numpy=\n",
       " array([0.00326888, 0.00769622, 0.00452655, 0.00993723, 0.00437781,\n",
       "        0.0041926 , 0.01119712, 0.00301442, 0.00646735, 0.00119985,\n",
       "        0.00795788, 0.0070232 , 0.00388592, 0.00863121, 0.00731359,\n",
       "        0.01336664, 0.00912285, 0.00850354, 0.00714551, 0.00913987,\n",
       "        0.00739679, 0.00598037, 0.00566064, 0.00406198, 0.00381998,\n",
       "        0.00955725, 0.00481013, 0.00927456, 0.0148468 , 0.00074255,\n",
       "        0.01121969, 0.00747218, 0.00726547, 0.01175072, 0.01133757,\n",
       "        0.00331001, 0.00545011, 0.00609958, 0.00631037, 0.01528351,\n",
       "        0.00507367, 0.00357197, 0.00502921, 0.01056105, 0.00489652,\n",
       "        0.00162046, 0.00833675, 0.00990053, 0.00539768, 0.00985801,\n",
       "        0.01227589, 0.01205054, 0.0032343 , 0.00241076, 0.00648142,\n",
       "        0.00491416, 0.00602806, 0.0083292 , 0.01112253, 0.00278629,\n",
       "        0.00430657, 0.00403413, 0.00936378, 0.01209203, 0.00248425,\n",
       "        0.01268505, 0.00931633, 0.00622042, 0.00700527, 0.00298896,\n",
       "        0.01002555, 0.01224328, 0.00349952, 0.00628744, 0.00900685,\n",
       "        0.00353767, 0.00594358, 0.01011317, 0.00665472, 0.00533639,\n",
       "        0.00492744, 0.00864756, 0.01440187, 0.00379589, 0.00943264,\n",
       "        0.01125312, 0.00945392, 0.00456466, 0.00726071, 0.00588875,\n",
       "        0.00277661, 0.00758977, 0.00798306, 0.00860315, 0.00929269,\n",
       "        0.00199701, 0.01189897, 0.00349746, 0.00516229, 0.00522694,\n",
       "        0.0057579 , 0.0011593 , 0.00535797, 0.01012401, 0.00575477,\n",
       "        0.00487423, 0.00731165, 0.00848369, 0.0103772 , 0.00325112,\n",
       "        0.00453239, 0.0100721 , 0.00659468, 0.01165404, 0.00480973,\n",
       "        0.00143427, 0.00475885, 0.01115377, 0.0149851 , 0.0069998 ,\n",
       "        0.00567213, 0.00150134, 0.00201901, 0.00367787, 0.00361393,\n",
       "        0.01358704, 0.00807572, 0.01089008, 0.00592643, 0.01203771,\n",
       "        0.00523088, 0.01289458, 0.0091787 , 0.00413799, 0.00819174,\n",
       "        0.00854219, 0.00703806, 0.00795153, 0.0036631 , 0.00435803,\n",
       "        0.00952367, 0.00552985, 0.00663754, 0.01153292, 0.00240665,\n",
       "        0.00685988, 0.01613734, 0.0080892 , 0.00338366, 0.01030747,\n",
       "        0.00728541, 0.00429572, 0.01314315, 0.00662926, 0.00568325,\n",
       "        0.01082489, 0.00712636, 0.00849945, 0.00599759, 0.0101677 ,\n",
       "        0.00439128, 0.00174609, 0.00431982, 0.00641554, 0.00284113,\n",
       "        0.00308888, 0.01031244, 0.008101  , 0.00996573, 0.01583866,\n",
       "        0.00742376, 0.00622952, 0.00971249, 0.01208168, 0.00342125,\n",
       "        0.0040439 , 0.00606887, 0.00998322, 0.00465499, 0.00633567,\n",
       "        0.00626865, 0.00705161, 0.00465834, 0.0102882 , 0.0088803 ,\n",
       "        0.00820661, 0.00555167, 0.00449756, 0.0030652 , 0.00533379,\n",
       "        0.00448687, 0.01162931, 0.01024735, 0.0070347 , 0.00685071,\n",
       "        0.01367166, 0.00481908, 0.00897652, 0.01520971, 0.00834879,\n",
       "        0.00523778, 0.00528132, 0.01191065, 0.00424349, 0.01322096,\n",
       "        0.00417171, 0.00715284, 0.00433546, 0.00546046, 0.00302912,\n",
       "        0.00439681, 0.01531436, 0.00630953, 0.00635568, 0.00738414,\n",
       "        0.01453385, 0.0097558 , 0.00938384, 0.00386689, 0.00310816,\n",
       "        0.00818303, 0.00839665, 0.00222524, 0.01043753, 0.00659275,\n",
       "        0.00810309, 0.0116295 , 0.01712324, 0.00312823, 0.00433289,\n",
       "        0.00559646, 0.00675354, 0.00758389, 0.0064624 , 0.0044468 ,\n",
       "        0.01043304, 0.00468923, 0.00314873, 0.0068159 , 0.00562112,\n",
       "        0.00803354, 0.00587673, 0.00322696, 0.00242001, 0.00744028,\n",
       "        0.00653226, 0.0056005 , 0.01182018, 0.01275641, 0.00556579,\n",
       "        0.00776744, 0.00889007, 0.00686995, 0.00303797, 0.01116353,\n",
       "        0.01310213], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_3/gamma:0' shape=(256,) dtype=float32, numpy=\n",
       " array([1.0241408, 1.0296016, 1.0391902, 1.0250382, 1.0247374, 1.0289564,\n",
       "        1.032919 , 1.0284257, 1.0390637, 1.0208359, 1.0265689, 1.0251029,\n",
       "        1.0173506, 1.0380582, 1.0205786, 1.0297501, 1.0276024, 1.0359156,\n",
       "        1.0123904, 1.0267745, 1.0204937, 1.025249 , 1.0244683, 1.0156757,\n",
       "        1.0222671, 1.0310668, 1.0249126, 1.0259742, 1.0333607, 1.023765 ,\n",
       "        1.0358671, 1.0279526, 1.0282991, 1.0285385, 1.0340927, 1.0250077,\n",
       "        1.0307392, 1.0312194, 1.0275272, 1.0323788, 1.0243474, 1.0335181,\n",
       "        1.0291454, 1.02429  , 1.0284038, 1.0245613, 1.0386314, 1.0271587,\n",
       "        1.0265822, 1.0302886, 1.0214707, 1.0263811, 1.0312247, 1.0330563,\n",
       "        1.030827 , 1.025069 , 1.0216558, 1.0284563, 1.0232992, 1.016152 ,\n",
       "        1.0243754, 1.0304066, 1.0299911, 1.023424 , 1.017413 , 1.0315022,\n",
       "        1.0211706, 1.0325229, 1.0253216, 1.0199372, 1.0280467, 1.0308658,\n",
       "        1.0181434, 1.0307603, 1.0207545, 1.024734 , 1.032826 , 1.0280229,\n",
       "        1.025485 , 1.02806  , 1.0264231, 1.0269605, 1.0283283, 1.0334699,\n",
       "        1.0294373, 1.0343565, 1.0345317, 1.0281216, 1.0163039, 1.0229039,\n",
       "        1.0259104, 1.0284487, 1.0228578, 1.0162055, 1.0335793, 1.0222869,\n",
       "        1.0221355, 1.026127 , 1.0405431, 1.0196446, 1.0224311, 1.0297755,\n",
       "        1.0353732, 1.0258455, 1.0238733, 1.0257361, 1.0289835, 1.0336361,\n",
       "        1.0256529, 1.0262642, 1.0250351, 1.0337653, 1.0345415, 1.0357391,\n",
       "        1.0244795, 1.0262336, 1.0239744, 1.0342768, 1.034358 , 1.0313016,\n",
       "        1.0219796, 1.0212549, 1.0185472, 1.0223004, 1.025965 , 1.0290406,\n",
       "        1.0298363, 1.0344496, 1.0303136, 1.0252802, 1.0213625, 1.0276089,\n",
       "        1.0347116, 1.0351427, 1.0347167, 1.0236777, 1.03178  , 1.0289601,\n",
       "        1.0245656, 1.0337875, 1.0193632, 1.021497 , 1.0256597, 1.0252918,\n",
       "        1.0213585, 1.0299659, 1.0351509, 1.0188434, 1.0263939, 1.0306225,\n",
       "        1.0301517, 1.0152825, 1.0230309, 1.0227661, 1.0259678, 1.0270224,\n",
       "        1.0345261, 1.0317625, 1.0276381, 1.0317048, 1.0294808, 1.0321306,\n",
       "        1.0250738, 1.0170791, 1.0191232, 1.0283499, 1.0399836, 1.0182267,\n",
       "        1.020251 , 1.0284688, 1.0316496, 1.0233091, 1.020489 , 1.0368994,\n",
       "        1.027328 , 1.020983 , 1.0223036, 1.0309708, 1.0209308, 1.031142 ,\n",
       "        1.0321405, 1.0252936, 1.0226754, 1.0251602, 1.0259485, 1.0275394,\n",
       "        1.0294937, 1.0234525, 1.0338876, 1.0340089, 1.0194722, 1.0299731,\n",
       "        1.0305992, 1.0284288, 1.0243032, 1.0356363, 1.028802 , 1.0347209,\n",
       "        1.0233411, 1.025335 , 1.0286961, 1.0240214, 1.0259675, 1.0112274,\n",
       "        1.030851 , 1.0313135, 1.0321436, 1.0293332, 1.029406 , 1.0224768,\n",
       "        1.0311409, 1.0349301, 1.0220697, 1.0163195, 1.026829 , 1.0332094,\n",
       "        1.0265878, 1.0240692, 1.0195563, 1.0222431, 1.0323857, 1.0272685,\n",
       "        1.0336719, 1.0168068, 1.0348525, 1.0311103, 1.0264544, 1.0240493,\n",
       "        1.0171597, 1.0202619, 1.0222472, 1.0298032, 1.0223836, 1.0281171,\n",
       "        1.0186412, 1.0299803, 1.0316124, 1.027024 , 1.03654  , 1.0242311,\n",
       "        1.0308628, 1.0197027, 1.0313501, 1.0267595, 1.0302799, 1.0392985,\n",
       "        1.0246449, 1.0190855, 1.0220431, 1.03375  , 1.0221705, 1.021208 ,\n",
       "        1.0295978, 1.0266122, 1.0327498, 1.0326241], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_3/beta:0' shape=(256,) dtype=float32, numpy=\n",
       " array([0.02228536, 0.03139082, 0.03965882, 0.02328644, 0.02649411,\n",
       "        0.02749745, 0.03383921, 0.02812673, 0.03721111, 0.02359001,\n",
       "        0.02721181, 0.02324072, 0.01614403, 0.03572025, 0.01723622,\n",
       "        0.02708969, 0.02379915, 0.03020197, 0.0085854 , 0.02498679,\n",
       "        0.01751774, 0.0311687 , 0.02725469, 0.01846135, 0.02418283,\n",
       "        0.02679662, 0.02528729, 0.02543875, 0.03194406, 0.02975911,\n",
       "        0.03390081, 0.0267869 , 0.02866863, 0.0290988 , 0.03537999,\n",
       "        0.0279998 , 0.02854683, 0.03054428, 0.02718715, 0.02591931,\n",
       "        0.02611147, 0.03358122, 0.03087404, 0.01968722, 0.02733579,\n",
       "        0.03225011, 0.04056368, 0.0259399 , 0.02626443, 0.02475841,\n",
       "        0.01553302, 0.0231329 , 0.03265992, 0.03277959, 0.03050816,\n",
       "        0.0242337 , 0.02267349, 0.02703843, 0.02144054, 0.02026729,\n",
       "        0.02044268, 0.02864163, 0.03121455, 0.0186652 , 0.01943902,\n",
       "        0.02842901, 0.01662784, 0.03353101, 0.02346172, 0.01764958,\n",
       "        0.02279674, 0.02916642, 0.02167505, 0.03090359, 0.01991782,\n",
       "        0.03037827, 0.03225022, 0.02197685, 0.02468815, 0.02985524,\n",
       "        0.02869355, 0.02091002, 0.02486869, 0.03278353, 0.02959449,\n",
       "        0.0291301 , 0.03605615, 0.03229861, 0.01469253, 0.02242816,\n",
       "        0.03085243, 0.02689124, 0.02371763, 0.0181626 , 0.02998288,\n",
       "        0.02373443, 0.01682846, 0.03007904, 0.03747323, 0.0210323 ,\n",
       "        0.02179429, 0.03207304, 0.03297742, 0.02233271, 0.0245086 ,\n",
       "        0.02289662, 0.02977012, 0.0308295 , 0.0262118 , 0.02845291,\n",
       "        0.02732059, 0.03222856, 0.037027  , 0.03632561, 0.02640976,\n",
       "        0.02804886, 0.02438865, 0.0310741 , 0.03254686, 0.03301262,\n",
       "        0.01867637, 0.02316975, 0.02098162, 0.02253862, 0.03133465,\n",
       "        0.02623134, 0.0297826 , 0.03457594, 0.03189835, 0.02438009,\n",
       "        0.02066261, 0.02176005, 0.03732819, 0.0333262 , 0.03134233,\n",
       "        0.01534348, 0.0323872 , 0.02980449, 0.02628256, 0.03526201,\n",
       "        0.01817159, 0.02309951, 0.02519501, 0.025836  , 0.02098051,\n",
       "        0.03292251, 0.03156833, 0.0193392 , 0.02791525, 0.02864891,\n",
       "        0.03313391, 0.01468677, 0.02306309, 0.02510644, 0.0277114 ,\n",
       "        0.02053213, 0.03873851, 0.02873201, 0.02645362, 0.0299667 ,\n",
       "        0.03101045, 0.0360547 , 0.02416763, 0.01558231, 0.02259988,\n",
       "        0.02688768, 0.03701255, 0.01387771, 0.01634868, 0.02367179,\n",
       "        0.0312247 , 0.02168311, 0.01910633, 0.03841808, 0.0320179 ,\n",
       "        0.02398808, 0.01923633, 0.02916239, 0.02033919, 0.02895651,\n",
       "        0.03081492, 0.01945724, 0.02220729, 0.02131232, 0.02672193,\n",
       "        0.02627072, 0.02754599, 0.02436562, 0.03875959, 0.03153821,\n",
       "        0.01794145, 0.02304294, 0.03153313, 0.02757192, 0.02309746,\n",
       "        0.03056989, 0.02844895, 0.02978   , 0.02156051, 0.02383444,\n",
       "        0.0289447 , 0.02472087, 0.02205544, 0.00941471, 0.02582198,\n",
       "        0.03642927, 0.03049504, 0.02732913, 0.02803811, 0.02150645,\n",
       "        0.03062914, 0.03510848, 0.01764094, 0.01367357, 0.0237079 ,\n",
       "        0.02833161, 0.0241022 , 0.01940913, 0.02960971, 0.02529293,\n",
       "        0.03449094, 0.02433617, 0.03260298, 0.01345859, 0.03787993,\n",
       "        0.03133486, 0.02381289, 0.01946264, 0.01647183, 0.02310165,\n",
       "        0.02211786, 0.03318695, 0.01953907, 0.02680669, 0.0201572 ,\n",
       "        0.02556855, 0.03475577, 0.03178848, 0.0334545 , 0.02368664,\n",
       "        0.02733218, 0.02369232, 0.03413324, 0.03014701, 0.02754448,\n",
       "        0.0412627 , 0.02330311, 0.01381864, 0.01848124, 0.03180942,\n",
       "        0.02199271, 0.02004548, 0.02691523, 0.02962959, 0.03241167,\n",
       "        0.03102869], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_3/moving_mean:0' shape=(256,) dtype=float32, numpy=\n",
       " array([0.1580883 , 0.19189106, 0.22526099, 0.11978672, 0.19457205,\n",
       "        0.16027388, 0.15621547, 0.46066463, 0.24949326, 0.31239328,\n",
       "        0.2288128 , 0.36604896, 0.35550255, 0.2423863 , 0.15445845,\n",
       "        0.20618658, 0.3064543 , 0.14338593, 0.19528057, 0.1385901 ,\n",
       "        0.10619579, 0.39145976, 0.4077503 , 0.2551286 , 0.25617254,\n",
       "        0.3046051 , 0.11380778, 0.13025872, 0.14732331, 0.26629364,\n",
       "        0.15714936, 0.15259923, 0.23030932, 0.20235713, 0.2510975 ,\n",
       "        0.24875733, 0.22019835, 0.35288316, 0.26792374, 0.09283122,\n",
       "        0.2981972 , 0.21012719, 0.44046533, 0.1404253 , 0.18414867,\n",
       "        0.40590382, 0.17731918, 0.23799968, 0.20371993, 0.15678611,\n",
       "        0.12255401, 0.127108  , 0.363886  , 0.19758177, 0.2733574 ,\n",
       "        0.14633822, 0.28250083, 0.14708531, 0.16819422, 0.68824893,\n",
       "        0.16264494, 0.20340168, 0.14571598, 0.13744517, 0.3196041 ,\n",
       "        0.19896093, 0.14859217, 0.15694322, 0.24444076, 0.29320467,\n",
       "        0.18423656, 0.17264521, 0.42346486, 0.15471262, 0.18659331,\n",
       "        0.5771252 , 0.21985261, 0.06615183, 0.22652684, 0.24403104,\n",
       "        0.2793515 , 0.15905404, 0.16025692, 0.23033306, 0.26981178,\n",
       "        0.11141872, 0.22078124, 0.1851176 , 0.19874072, 0.31775478,\n",
       "        0.33913773, 0.1794348 , 0.24779795, 0.34874794, 0.14252482,\n",
       "        0.55400175, 0.11504518, 0.35493273, 0.20942678, 0.17001371,\n",
       "        0.22853954, 0.4182969 , 0.23561937, 0.16636275, 0.23795174,\n",
       "        0.21797529, 0.29720375, 0.15776236, 0.12375487, 0.28600818,\n",
       "        0.29664442, 0.20547567, 0.18148577, 0.22050928, 0.13909604,\n",
       "        0.28052887, 0.27104306, 0.11354506, 0.1347719 , 0.41091332,\n",
       "        0.16806036, 0.35568988, 0.5395138 , 0.3685354 , 0.20234933,\n",
       "        0.20251602, 0.18064062, 0.16352403, 0.24452502, 0.23050673,\n",
       "        0.3332712 , 0.12084363, 0.24892724, 0.28432572, 0.18123338,\n",
       "        0.08592592, 0.12524745, 0.18515952, 0.32740057, 0.36966607,\n",
       "        0.24428144, 0.23966458, 0.270169  , 0.18246509, 0.29273772,\n",
       "        0.28149173, 0.17752679, 0.29714748, 0.313912  , 0.19949813,\n",
       "        0.33746687, 0.34815815, 0.17545167, 0.2642271 , 0.34582043,\n",
       "        0.12339693, 0.33593225, 0.12671176, 0.27936554, 0.24585268,\n",
       "        0.32359755, 0.40161592, 0.26136363, 0.15331043, 0.33358002,\n",
       "        0.4090599 , 0.24403945, 0.12674344, 0.13037042, 0.13189383,\n",
       "        0.1916026 , 0.17268842, 0.16218373, 0.3420265 , 0.45548528,\n",
       "        0.31351644, 0.20522915, 0.14504327, 0.30553263, 0.2013492 ,\n",
       "        0.15342651, 0.17311423, 0.2669315 , 0.09857185, 0.15450306,\n",
       "        0.26975477, 0.1918272 , 0.311705  , 0.46187484, 0.21026464,\n",
       "        0.46101725, 0.07949319, 0.29160315, 0.28329113, 0.18311521,\n",
       "        0.17054269, 0.15184818, 0.10347736, 0.257284  , 0.19509245,\n",
       "        0.15551962, 0.212872  , 0.18546051, 0.27321938, 0.11761771,\n",
       "        0.36691254, 0.11167013, 0.19595285, 0.14517148, 0.30531988,\n",
       "        0.24186552, 0.10991961, 0.0818816 , 0.22850412, 0.20662965,\n",
       "        0.22038169, 0.19023566, 0.16881095, 0.38232824, 0.4504251 ,\n",
       "        0.19784755, 0.13895303, 0.3197791 , 0.16893671, 0.3542006 ,\n",
       "        0.19024451, 0.18348274, 0.09370078, 0.35212082, 0.29594386,\n",
       "        0.15385689, 0.22725405, 0.22705235, 0.22787584, 0.24437244,\n",
       "        0.19804424, 0.24276616, 0.4648007 , 0.20916852, 0.22536585,\n",
       "        0.21317653, 0.3133828 , 0.38605556, 0.20913208, 0.19040616,\n",
       "        0.4514993 , 0.3738684 , 0.05668831, 0.14803168, 0.21862979,\n",
       "        0.15951616, 0.28327194, 0.14563735, 0.26029092, 0.19507357,\n",
       "        0.14481792], dtype=float32)>,\n",
       " <tf.Variable 'batch_normalization_3/moving_variance:0' shape=(256,) dtype=float32, numpy=\n",
       " array([0.08261532, 0.09511627, 0.11447851, 0.0525123 , 0.09559585,\n",
       "        0.06594004, 0.05053762, 0.30918044, 0.13876446, 0.18052791,\n",
       "        0.10553202, 0.28676555, 0.38867703, 0.13847744, 0.07390983,\n",
       "        0.10565878, 0.1936383 , 0.0604832 , 0.14594716, 0.0708358 ,\n",
       "        0.05417994, 0.19755355, 0.28114098, 0.13233052, 0.12976688,\n",
       "        0.24265341, 0.04928716, 0.05060072, 0.0693194 , 0.12398646,\n",
       "        0.05852218, 0.07337447, 0.11358199, 0.07551246, 0.11627812,\n",
       "        0.14842045, 0.11698032, 0.24489099, 0.14967741, 0.03502459,\n",
       "        0.16648059, 0.09349057, 0.28427434, 0.07773053, 0.08411478,\n",
       "        0.17654671, 0.07885583, 0.12560438, 0.10152712, 0.08131643,\n",
       "        0.05466272, 0.05356022, 0.24529067, 0.07644372, 0.15106793,\n",
       "        0.05727871, 0.17447308, 0.06066691, 0.07322451, 0.95994884,\n",
       "        0.07286365, 0.09911635, 0.05250891, 0.05559185, 0.20871514,\n",
       "        0.08772852, 0.07852743, 0.0589579 , 0.14532608, 0.23380049,\n",
       "        0.11777782, 0.09360689, 0.40327206, 0.05764369, 0.10642432,\n",
       "        0.35432878, 0.1271177 , 0.02550222, 0.15092775, 0.11246485,\n",
       "        0.14000298, 0.09270468, 0.07336761, 0.11179118, 0.162915  ,\n",
       "        0.04506852, 0.11230039, 0.08430228, 0.12279522, 0.23564133,\n",
       "        0.17975855, 0.0755164 , 0.11306431, 0.24829203, 0.07822172,\n",
       "        0.58522034, 0.04645337, 0.19931078, 0.12006889, 0.0794751 ,\n",
       "        0.14509389, 0.3084395 , 0.13016982, 0.07030919, 0.10401534,\n",
       "        0.14522994, 0.14750098, 0.07130653, 0.05546203, 0.15393515,\n",
       "        0.14734039, 0.09679625, 0.08654372, 0.10834122, 0.07180788,\n",
       "        0.16803756, 0.14907557, 0.04099333, 0.05086681, 0.27429867,\n",
       "        0.09564526, 0.20576926, 0.53893286, 0.3660225 , 0.09423816,\n",
       "        0.09483889, 0.08895175, 0.06698776, 0.11031493, 0.11815523,\n",
       "        0.2712449 , 0.06236779, 0.12573233, 0.18849728, 0.08922699,\n",
       "        0.04744231, 0.04727282, 0.07722297, 0.21583393, 0.19769032,\n",
       "        0.10815157, 0.13508639, 0.12856707, 0.08157092, 0.14023505,\n",
       "        0.13414778, 0.08511834, 0.21421833, 0.18256862, 0.10699811,\n",
       "        0.1752393 , 0.35231957, 0.07190983, 0.152239  , 0.2023395 ,\n",
       "        0.06199361, 0.18679053, 0.04837184, 0.17438093, 0.11459042,\n",
       "        0.20561212, 0.22129321, 0.1291278 , 0.08711267, 0.1994295 ,\n",
       "        0.39222777, 0.10859906, 0.10120378, 0.05854923, 0.06309737,\n",
       "        0.12520543, 0.07920498, 0.06935076, 0.19099332, 0.27310115,\n",
       "        0.19860521, 0.11619478, 0.06806976, 0.20252971, 0.09681625,\n",
       "        0.07453791, 0.12742369, 0.13746287, 0.04280583, 0.06857899,\n",
       "        0.14252546, 0.10331832, 0.21337666, 0.2911224 , 0.0959549 ,\n",
       "        0.7708768 , 0.02976279, 0.15101734, 0.16785051, 0.07712325,\n",
       "        0.07386414, 0.06199658, 0.05148453, 0.13857917, 0.09259344,\n",
       "        0.08935113, 0.13057937, 0.09721183, 0.31291637, 0.04440888,\n",
       "        0.23188664, 0.04761041, 0.09235922, 0.06569931, 0.22401915,\n",
       "        0.1390409 , 0.04248218, 0.03120583, 0.20484729, 0.12399402,\n",
       "        0.11787104, 0.09656794, 0.1122067 , 0.17420758, 0.35295954,\n",
       "        0.08185378, 0.05927282, 0.18796028, 0.09010302, 0.1742803 ,\n",
       "        0.09563771, 0.10214525, 0.03895476, 0.32724002, 0.16513431,\n",
       "        0.07681072, 0.10120577, 0.16811465, 0.10431816, 0.14672145,\n",
       "        0.09797347, 0.12037054, 0.31814295, 0.12697063, 0.12521072,\n",
       "        0.12191451, 0.18979187, 0.2407898 , 0.08684652, 0.07630455,\n",
       "        0.31450346, 0.27397773, 0.02144966, 0.0763435 , 0.14207424,\n",
       "        0.08357768, 0.18916461, 0.08493768, 0.15678312, 0.09396233,\n",
       "        0.05840444], dtype=float32)>,\n",
       " <tf.Variable 'dense/kernel:0' shape=(12032, 3) dtype=float32, numpy=\n",
       " array([[0.04654273, 0.03966781, 0.0049111 ],\n",
       "        [0.00968216, 0.01736498, 0.03180144],\n",
       "        [0.04621651, 0.01119971, 0.02797985],\n",
       "        ...,\n",
       "        [0.00628518, 0.03826421, 0.01350075],\n",
       "        [0.03332352, 0.01219184, 0.00324796],\n",
       "        [0.02908309, 0.04351445, 0.03098816]], dtype=float32)>,\n",
       " <tf.Variable 'dense/bias:0' shape=(3,) dtype=float32, numpy=array([0.07632568, 0.04404109, 0.04516717], dtype=float32)>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing using HeartPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heartpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_number = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = X_test[sample_number]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data, measures = heartpy.process(sample, sample_rate=100.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heartpy.plotter(working_data=working_data, measures=measures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bpm: 83.916084\n",
      "ibi: 715.000000\n",
      "sdnn: 5.000000\n",
      "sdsd: 0.000000\n",
      "rmssd: 10.000000\n",
      "pnn20: 0.000000\n",
      "pnn50: 0.000000\n",
      "hr_mad: 5.000000\n",
      "sd1: 0.000000\n",
      "sd2: 0.000000\n",
      "s: 0.000000\n",
      "sd1/sd2: nan\n",
      "breathingrate: nan\n"
     ]
    }
   ],
   "source": [
    "for measure in measures.keys():\n",
    "    print('%s: %f' % (measure, measures[measure]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([96, 67, 82], dtype=int64)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[sample_number]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([126.517426,  75.35723 ,  88.18448 ], dtype=float32)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[sample_number]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
